scientific thesis definition

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What is a thesis | A Complete Guide with Examples

Table of Contents

A thesis is a comprehensive academic paper based on your original research that presents new findings, arguments, and ideas of your study. It’s typically submitted at the end of your master’s degree or as a capstone of your bachelor’s degree.

However, writing a thesis can be laborious, especially for beginners. From the initial challenge of pinpointing a compelling research topic to organizing and presenting findings, the process is filled with potential pitfalls.

Therefore, to help you, this guide talks about what is a thesis. Additionally, it offers revelations and methodologies to transform it from an overwhelming task to a manageable and rewarding academic milestone.

What is a thesis?

A thesis is an in-depth research study that identifies a particular topic of inquiry and presents a clear argument or perspective about that topic using evidence and logic.

Writing a thesis showcases your ability of critical thinking, gathering evidence, and making a compelling argument. Integral to these competencies is thorough research, which not only fortifies your propositions but also confers credibility to your entire study.

Furthermore, there's another phenomenon you might often confuse with the thesis: the ' working thesis .' However, they aren't similar and shouldn't be used interchangeably.

A working thesis, often referred to as a preliminary or tentative thesis, is an initial version of your thesis statement. It serves as a draft or a starting point that guides your research in its early stages.

As you research more and gather more evidence, your initial thesis (aka working thesis) might change. It's like a starting point that can be adjusted as you learn more. It's normal for your main topic to change a few times before you finalize it.

While a thesis identifies and provides an overarching argument, the key to clearly communicating the central point of that argument lies in writing a strong thesis statement.

What is a thesis statement?

A strong thesis statement (aka thesis sentence) is a concise summary of the main argument or claim of the paper. It serves as a critical anchor in any academic work, succinctly encapsulating the primary argument or main idea of the entire paper.

Typically found within the introductory section, a strong thesis statement acts as a roadmap of your thesis, directing readers through your arguments and findings. By delineating the core focus of your investigation, it offers readers an immediate understanding of the context and the gravity of your study.

Furthermore, an effectively crafted thesis statement can set forth the boundaries of your research, helping readers anticipate the specific areas of inquiry you are addressing.

Different types of thesis statements

A good thesis statement is clear, specific, and arguable. Therefore, it is necessary for you to choose the right type of thesis statement for your academic papers.

Thesis statements can be classified based on their purpose and structure. Here are the primary types of thesis statements:

Argumentative (or Persuasive) thesis statement

Purpose : To convince the reader of a particular stance or point of view by presenting evidence and formulating a compelling argument.

Example : Reducing plastic use in daily life is essential for environmental health.

Analytical thesis statement

Purpose : To break down an idea or issue into its components and evaluate it.

Example : By examining the long-term effects, social implications, and economic impact of climate change, it becomes evident that immediate global action is necessary.

Expository (or Descriptive) thesis statement

Purpose : To explain a topic or subject to the reader.

Example : The Great Depression, spanning the 1930s, was a severe worldwide economic downturn triggered by a stock market crash, bank failures, and reduced consumer spending.

Cause and effect thesis statement

Purpose : To demonstrate a cause and its resulting effect.

Example : Overuse of smartphones can lead to impaired sleep patterns, reduced face-to-face social interactions, and increased levels of anxiety.

Compare and contrast thesis statement

Purpose : To highlight similarities and differences between two subjects.

Example : "While both novels '1984' and 'Brave New World' delve into dystopian futures, they differ in their portrayal of individual freedom, societal control, and the role of technology."

When you write a thesis statement , it's important to ensure clarity and precision, so the reader immediately understands the central focus of your work.

What is the difference between a thesis and a thesis statement?

While both terms are frequently used interchangeably, they have distinct meanings.

A thesis refers to the entire research document, encompassing all its chapters and sections. In contrast, a thesis statement is a brief assertion that encapsulates the central argument of the research.

Here’s an in-depth differentiation table of a thesis and a thesis statement.

Now, to craft a compelling thesis, it's crucial to adhere to a specific structure. Let’s break down these essential components that make up a thesis structure

15 components of a thesis structure

Navigating a thesis can be daunting. However, understanding its structure can make the process more manageable.

Here are the key components or different sections of a thesis structure:

Your thesis begins with the title page. It's not just a formality but the gateway to your research.

Here, you'll prominently display the necessary information about you (the author) and your institutional details.

• Title of your thesis
• Your full name
• Your department
• Your institution and degree program
• Your submission date
• Your Supervisor's name (in some cases)
• Your Department or faculty (in some cases)
• Your University's logo (in some cases)
• Your Student ID (in some cases)

In a concise manner, you'll have to summarize the critical aspects of your research in typically no more than 200-300 words.

This includes the problem statement, methodology, key findings, and conclusions. For many, the abstract will determine if they delve deeper into your work, so ensure it's clear and compelling.

Acknowledgments

Research is rarely a solitary endeavor. In the acknowledgments section, you have the chance to express gratitude to those who've supported your journey.

This might include advisors, peers, institutions, or even personal sources of inspiration and support. It's a personal touch, reflecting the humanity behind the academic rigor.

Table of contents

A roadmap for your readers, the table of contents lists the chapters, sections, and subsections of your thesis.

By providing page numbers, you allow readers to navigate your work easily, jumping to sections that pique their interest.

List of figures and tables

Research often involves data, and presenting this data visually can enhance understanding. This section provides an organized listing of all figures and tables in your thesis.

It's a visual index, ensuring that readers can quickly locate and reference your graphical data.

Introduction

Here's where you introduce your research topic, articulate the research question or objective, and outline the significance of your study.

• Present the research topic : Clearly articulate the central theme or subject of your research.
• Background information : Ground your research topic, providing any necessary context or background information your readers might need to understand the significance of your study.
• Define the scope : Clearly delineate the boundaries of your research, indicating what will and won't be covered.
• Literature review : Introduce any relevant existing research on your topic, situating your work within the broader academic conversation and highlighting where your research fits in.
• State the research Question(s) or objective(s) : Clearly articulate the primary questions or objectives your research aims to address.
• Outline the study's structure : Give a brief overview of how the subsequent sections of your work will unfold, guiding your readers through the journey ahead.

The introduction should captivate your readers, making them eager to delve deeper into your research journey.

Literature review section

Your study correlates with existing research. Therefore, in the literature review section, you'll engage in a dialogue with existing knowledge, highlighting relevant studies, theories, and findings.

It's here that you identify gaps in the current knowledge, positioning your research as a bridge to new insights.

To streamline this process, consider leveraging AI tools. For example, the SciSpace literature review tool enables you to efficiently explore and delve into research papers, simplifying your literature review journey.

Methodology

In the research methodology section, you’ll detail the tools, techniques, and processes you employed to gather and analyze data. This section will inform the readers about how you approached your research questions and ensures the reproducibility of your study.

Here's a breakdown of what it should encompass:

• Research Design : Describe the overall structure and approach of your research. Are you conducting a qualitative study with in-depth interviews? Or is it a quantitative study using statistical analysis? Perhaps it's a mixed-methods approach?
• Data Collection : Detail the methods you used to gather data. This could include surveys, experiments, observations, interviews, archival research, etc. Mention where you sourced your data, the duration of data collection, and any tools or instruments used.
• Sampling : If applicable, explain how you selected participants or data sources for your study. Discuss the size of your sample and the rationale behind choosing it.
• Data Analysis : Describe the techniques and tools you used to process and analyze the data. This could range from statistical tests in quantitative research to thematic analysis in qualitative research.
• Validity and Reliability : Address the steps you took to ensure the validity and reliability of your findings to ensure that your results are both accurate and consistent.
• Ethical Considerations : Highlight any ethical issues related to your research and the measures you took to address them, including — informed consent, confidentiality, and data storage and protection measures.

Moreover, different research questions necessitate different types of methodologies. For instance:

• Experimental methodology : Often used in sciences, this involves a controlled experiment to discern causality.
• Qualitative methodology : Employed when exploring patterns or phenomena without numerical data. Methods can include interviews, focus groups, or content analysis.
• Quantitative methodology : Concerned with measurable data and often involves statistical analysis. Surveys and structured observations are common tools here.
• Mixed methods : As the name implies, this combines both qualitative and quantitative methodologies.

The Methodology section isn’t just about detailing the methods but also justifying why they were chosen. The appropriateness of the methods in addressing your research question can significantly impact the credibility of your findings.

Results (or Findings)

This section presents the outcomes of your research. It's crucial to note that the nature of your results may vary; they could be quantitative, qualitative, or a mix of both.

Quantitative results often present statistical data, showcasing measurable outcomes, and they benefit from tables, graphs, and figures to depict these data points.

Qualitative results , on the other hand, might delve into patterns, themes, or narratives derived from non-numerical data, such as interviews or observations.

Regardless of the nature of your results, clarity is essential. This section is purely about presenting the data without offering interpretations — that comes later in the discussion.

In the discussion section, the raw data transforms into valuable insights.

Start by revisiting your research question and contrast it with the findings. How do your results expand, constrict, or challenge current academic conversations?

Dive into the intricacies of the data, guiding the reader through its implications. Detail potential limitations transparently, signaling your awareness of the research's boundaries. This is where your academic voice should be resonant and confident.

Practical implications (Recommendation) section

Based on the insights derived from your research, this section provides actionable suggestions or proposed solutions.

Whether aimed at industry professionals or the general public, recommendations translate your academic findings into potential real-world actions. They help readers understand the practical implications of your work and how it can be applied to effect change or improvement in a given field.

When crafting recommendations, it's essential to ensure they're feasible and rooted in the evidence provided by your research. They shouldn't merely be aspirational but should offer a clear path forward, grounded in your findings.

The conclusion provides closure to your research narrative.

It's not merely a recap but a synthesis of your main findings and their broader implications. Reconnect with the research questions or hypotheses posited at the beginning, offering clear answers based on your findings.

Reflect on the broader contributions of your study, considering its impact on the academic community and potential real-world applications.

Lastly, the conclusion should leave your readers with a clear understanding of the value and impact of your study.

References (or Bibliography)

Every theory you've expounded upon, every data point you've cited, and every methodological precedent you've followed finds its acknowledgment here.

In references, it's crucial to ensure meticulous consistency in formatting, mirroring the specific guidelines of the chosen citation style .

Proper referencing helps to avoid plagiarism , gives credit to original ideas, and allows readers to explore topics of interest. Moreover, it situates your work within the continuum of academic knowledge.

To properly cite the sources used in the study, you can rely on online citation generator tools  to generate accurate citations!

Here’s more on how you can cite your sources.

Often, the depth of research produces a wealth of material that, while crucial, can make the core content of the thesis cumbersome. The appendix is where you mention extra information that supports your research but isn't central to the main text.

Whether it's raw datasets, detailed procedural methodologies, extended case studies, or any other ancillary material, the appendices ensure that these elements are archived for reference without breaking the main narrative's flow.

For thorough researchers and readers keen on meticulous details, the appendices provide a treasure trove of insights.

Glossary (optional)

In academics, specialized terminologies, and jargon are inevitable. However, not every reader is versed in every term.

The glossary, while optional, is a critical tool for accessibility. It's a bridge ensuring that even readers from outside the discipline can access, understand, and appreciate your work.

By defining complex terms and providing context, you're inviting a wider audience to engage with your research, enhancing its reach and impact.

Remember, while these components provide a structured framework, the essence of your thesis lies in the originality of your ideas, the rigor of your research, and the clarity of your presentation.

As you craft each section, keep your readers in mind, ensuring that your passion and dedication shine through every page.

Thesis examples

To further elucidate the concept of a thesis, here are illustrative examples from various fields:

Example 1 (History): Abolition, Africans, and Abstraction: the Influence of the ‘Noble Savage’ on British and French Antislavery Thought, 1787-1807 by Suchait Kahlon.

Example 2 (Climate Dynamics): Influence of external forcings on abrupt millennial-scale climate changes: a statistical modelling study by Takahito Mitsui · Michel Crucifix

Checklist for your thesis evaluation

Evaluating your thesis ensures that your research meets the standards of academia. Here's an elaborate checklist to guide you through this critical process.

Content and structure

• Is the thesis statement clear, concise, and debatable?
• Does the introduction provide sufficient background and context?
• Is the literature review comprehensive, relevant, and well-organized?
• Does the methodology section clearly describe and justify the research methods?
• Are the results/findings presented clearly and logically?
• Does the discussion interpret the results in light of the research question and existing literature?
• Is the conclusion summarizing the research and suggesting future directions or implications?

Clarity and coherence

• Is the writing clear and free of jargon?
• Are ideas and sections logically connected and flowing?
• Is there a clear narrative or argument throughout the thesis?

Research quality

• Is the research question significant and relevant?
• Are the research methods appropriate for the question?
• Is the sample size (if applicable) adequate?
• Are the data analysis techniques appropriate and correctly applied?
• Are potential biases or limitations addressed?

Originality and significance

• Does the thesis contribute new knowledge or insights to the field?
• Is the research grounded in existing literature while offering fresh perspectives?

Formatting and presentation

• Is the thesis formatted according to institutional guidelines?
• Are figures, tables, and charts clear, labeled, and referenced in the text?
• Is the bibliography or reference list complete and consistently formatted?
• Are appendices relevant and appropriately referenced in the main text?

Grammar and language

• Is the thesis free of grammatical and spelling errors?
• Is the language professional, consistent, and appropriate for an academic audience?
• Are quotations and paraphrased material correctly cited?

Feedback and revision

• Have you sought feedback from peers, advisors, or experts in the field?
• Have you addressed the feedback and made the necessary revisions?

Overall assessment

• Does the thesis as a whole feel cohesive and comprehensive?
• Would the thesis be understandable and valuable to someone in your field?

Ensure to use this checklist to leave no ground for doubt or missed information in your thesis.

After writing your thesis, the next step is to discuss and defend your findings verbally in front of a knowledgeable panel. You’ve to be well prepared as your professors may grade your presentation abilities.

Preparing your thesis defense

A thesis defense, also known as "defending the thesis," is the culmination of a scholar's research journey. It's the final frontier, where you’ll present their findings and face scrutiny from a panel of experts.

Typically, the defense involves a public presentation where you’ll have to outline your study, followed by a question-and-answer session with a committee of experts. This committee assesses the validity, originality, and significance of the research.

The defense serves as a rite of passage for scholars. It's an opportunity to showcase expertise, address criticisms, and refine arguments. A successful defense not only validates the research but also establishes your authority as a researcher in your field.

Here’s how you can effectively prepare for your thesis defense .

Now, having touched upon the process of defending a thesis, it's worth noting that scholarly work can take various forms, depending on academic and regional practices.

One such form, often paralleled with the thesis, is the 'dissertation.' But what differentiates the two?

Dissertation vs. Thesis

Often used interchangeably in casual discourse, they refer to distinct research projects undertaken at different levels of higher education.

To the uninitiated, understanding their meaning might be elusive. So, let's demystify these terms and delve into their core differences.

Here's a table differentiating between the two.

Wrapping up

From understanding the foundational concept of a thesis to navigating its various components, differentiating it from a dissertation, and recognizing the importance of proper citation — this guide covers it all.

As scholars and readers, understanding these nuances not only aids in academic pursuits but also fosters a deeper appreciation for the relentless quest for knowledge that drives academia.

It’s important to remember that every thesis is a testament to curiosity, dedication, and the indomitable spirit of discovery.

Good luck with your thesis writing!

Frequently Asked Questions

A thesis typically ranges between 40-80 pages, but its length can vary based on the research topic, institution guidelines, and level of study.

A PhD thesis usually spans 200-300 pages, though this can vary based on the discipline, complexity of the research, and institutional requirements.

To identify a thesis topic, consider current trends in your field, gaps in existing literature, personal interests, and discussions with advisors or mentors. Additionally, reviewing related journals and conference proceedings can provide insights into potential areas of exploration.

The conceptual framework is often situated in the literature review or theoretical framework section of a thesis. It helps set the stage by providing the context, defining key concepts, and explaining the relationships between variables.

A thesis statement should be concise, clear, and specific. It should state the main argument or point of your research. Start by pinpointing the central question or issue your research addresses, then condense that into a single statement, ensuring it reflects the essence of your paper.

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Definition of thesis

Did you know.

In high school, college, or graduate school, students often have to write a thesis on a topic in their major field of study. In many fields, a final thesis is the biggest challenge involved in getting a master's degree, and the same is true for students studying for a Ph.D. (a Ph.D. thesis is often called a dissertation ). But a thesis may also be an idea; so in the course of the paper the student may put forth several theses (notice the plural form) and attempt to prove them.

Examples of thesis in a Sentence

These examples are programmatically compiled from various online sources to illustrate current usage of the word 'thesis.' Any opinions expressed in the examples do not represent those of Merriam-Webster or its editors. Send us feedback about these examples.

Word History

in sense 3, Middle English, lowering of the voice, from Late Latin & Greek; Late Latin, from Greek, downbeat, more important part of a foot, literally, act of laying down; in other senses, Latin, from Greek, literally, act of laying down, from tithenai to put, lay down — more at do

14th century, in the meaning defined at sense 3a(1)

Dictionary Entries Near thesis

the sins of the fathers are visited upon the children

thesis novel

Cite this Entry

“Thesis.” Merriam-Webster.com Dictionary , Merriam-Webster, https://www.merriam-webster.com/dictionary/thesis. Accessed 14 Apr. 2024.

Kids Definition

Kids definition of thesis, more from merriam-webster on thesis.

Nglish: Translation of thesis for Spanish Speakers

Britannica English: Translation of thesis for Arabic Speakers

Britannica.com: Encyclopedia article about thesis

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How to Write a Science Thesis/Dissertation

A thesis/dissertation is a long, high-level research paper written as the culmination of your academic course. Most university programs require that graduate and postgraduate students demonstrate their ability to perform original research at the thesis/dissertation level as a graduation requirement.

Not all theses/dissertations are structured the same way. In this article, we’ll specifically look at how to structure a thesis/dissertation in the sciences and examine what belongs in each section. Before you begin writing, it is essential to have a good understanding of how to structure your science thesis/dissertation and what elements you must include in it.

How are science theses/dissertations structured?

There isn’t a universal format for a science thesis/dissertation. Each university/institution has its own rules, and these rules can vary further by department and advisor. For this reason, you must start writing/drafting your thesis/dissertation by checking the rules and requirements of your university/institution.

Some universities mandate a minimum word count for a thesis/dissertation, while others provide a maximum. The number of words you are expected to write will also vary depending on the program/course you are a part of. A Master’s level thesis/dissertation can range, for example, from 15,000 to 45,000 words, while a PhD thesis/dissertation can be around 80,000 words.

While your university/institution may have its own specific requirements or guidelines, this article provides a general overview of how a typical thesis/dissertation in the sciences should be structured. For easier understanding, let’s break it up into two parts:

• Thesis body
• Supplemental information

The thesis body of your thesis/dissertation includes:

• Acknowledgements

Table of contents

Introduction/literature review, materials/methodology, discussion/conclusion, figure and tables, list of abbreviations.

Your thesis will conclude with the supplemental information section, which comprises:

Reference list

Your thesis may or may not include each and every one of these sections. Now, let’s examine the parts of a thesis/dissertation in greater detail.

The parts of a science thesis/dissertation: Getting started

Let’s begin by reviewing the sections of the thesis body, from the title page to the glossary. This part of your thesis/dissertation should ideally be written last, even though it comes at the beginning. That is because it is the easiest to put it togethe r once you have written the rest of your thesis/dissertation.

Your thesis/dissertation should have a clear title that sums up the content. In addition, the title page should include your name, the degree of your thesis/dissertation, your department, your advisor, and the month/year of submission. Your university/institution likely has its own format for what should be included in the title page, so make sure to check the relevant guidelines.

Acknowledgments

This section gives you the opportunity to say thanks to anyone who gave you support while you worked on your thesis/dissertation. Many people use this section to give credit to their advisor, editor, or even their parents. If you received any funding for your research or technical assistance, make sure to mention it here.

Your abstract should be a brief summary (generally around 300 words) of your thesis/dissertation. You can think of your abstract as a distillation of your thesis/dissertation as a whole. You need to summarize the scope and objectives, methods, and findings in this section.

 The table of contents is a directory of the various parts of your thesis/dissertation. It should include the headings and subheadings of each section along with the page numbers where those sections can be found.

 Think of this section as the table of contents for figures and tables in your thesis/dissertation. The titles of each figure/table and the page number where it can be found should be in this list.

This list is intended to identify specialized abbreviations used throughout your thesis/dissertation. This can include the names of organizations (WHO, CDC), acronyms (PFC), and so on. For a science thesis/dissertation, it is preferable also to include a note regarding any abbreviations for units of measurement and standard notations for chemical elements, formulae, and chemical abbreviations used.

In this section, you would define any terminology that your target audience may be unfamiliar with.

The parts of a science thesis/dissertation: Presenting your data

Following the glossary, the thesis body of a science thesis/dissertation begins with the introduction. The introduction section of a science thesis/dissertation often also includes the literature review. This is unlike most social science or humanities theses/dissertations, where the literature review commonly forms a separate chapter. The introduction section should begin by clearly stating the background and context for your research study, followed by your thesis question, objectives, hypothesis , and thesis statement . An example might be: 

“The connection between nicotine consumption and insulin resistance has long been established. However, there is no substantial body of research on how long insulin resistance is maintained after people quit smoking. In this study, we aim to measure levels of insulin resistance in otherwise healthy subjects following a total cessation of nicotine consumption. We hypothesize that insulin resistance will begin to decline rapidly within six months.”

 The introduction should be immediately followed by a review of earlier literature written on the thesis topic. In this section, you should also clearly identify where the literature connects to your study and how your research study fills a gap or bolsters previous studies. Fit your study within the puzzle of previous work and demonstrate the importance of your research.

In the methodology section of your thesis/dissertation, you must explain what you did and how you did it. If you used materials (for example, bacteria), make sure you clearly list each one. Live materials should be listed, including the specific strain and genus. You must explain your techniques, materials, and methods such that another researcher can replicate exactly what you have done.

In the results section, you will explain what happened. What were your findings? This section should be heavy on data and light on analysis. Usually, in-depth analysis and interpretation of your results will be covered in the discussion section of your thesis/dissertation. While you should present your results in full, any supplementary data that you don’t have room for can be included in an appendix. As a note, this section is often written in the past tense. While other portions of your thesis/dissertation may use past and present interchangeably depending on the topic at hand, the results section of a scientific paper focuses on what has already happened (in an experiment), which is why it is written this way.

In this part of your thesis/dissertation, you will discuss what your findings mean. Did they align with your hypothesis? If so, how? If not, what was different? If there were any exceptions, errors, or total lack of correlation found, do not try to hide it. Clearly discuss what it might mean, or if you aren’t sure, don’t be afraid to say so. In this section, you can also highlight potential practical applications for your research study, limitations of your study, directions for future studies, and once again highlight the importance of your study in the field. This section usually concludes with an overall summarization of whether your results support your hypothesis or not. For example:

“Our study found that 500 of our 600 subjects continued to exhibit high levels of insulin resistance three years or more after stopping nicotine use. This does not support our hypothesis that insulin resistance would begin to drop around six months after subjects stopped nicotine use. Further research is warranted into the mechanisms by which past nicotine use alters insulin resistance levels in former smokers.”

The reference list is an alphabetical or numerical list of sources you’ve used while researching and writing your thesis. The formatting of your reference list will be dependent on your university guidelines. Useful tools like citation generators can help you correctly format your references. Reference managers like EndNote or Mendeley are also helpful for compiling this list. Furthermore, a professional editor or proofreading service can ensure that each reference is correctly formatted.

This section can be very useful if you want to include materials that are relevant to the topic of your thesis/dissertation but that you were unable to include in the main text. Tables, large bodies of text, illustrations, forms used to collect data or perform studies, and other such materials can all be included in an appendix.

Critical steps for planning, drafting, and structuring a science thesis/dissertation

Writing your thesis/dissertation is a daunting and lengthy task. Here are some helpful tips to keep in mind when drafting your science thesis/dissertation:

• Choose a thesis topic that is of professional interest to you. You are going to spend a lot of time thinking, reading, and writing about your thesis topic. Many aspiring young researchers end up working in a field related to their thesis/dissertation . If you start researching or writing a proposal and then decide you aren’t into the topic, don’t be afraid to change directions!
• Plan your thesis timelines carefully. Is your topic realistic given the time and material constraints you have? Do you need to apply for external funding for your research study? Will that take additional time? Write a schedule and revisit/revise it often throughout your thesis/dissertation process.
• Don’t wait until the last minute to start writing! A thesis/dissertation isn’t like an undergraduate paper where you spend some time researching and then some time writing it. You will need to write your thesis/dissertation as you continue your research study. Write as you work in the lab. Write as you learn things and then revise. Ideally, by the time you have finished your actual research study, you will already have a substantive draft.
• Start writing the methodology section first. This is often the easiest because it is straightforward and you have already done quite a lot of the work while preparing your research study. The order in which you write your thesis/dissertation doesn’t matter too much—if you find yourself jumping between sections, that is perfectly normal.
• Keep a detailed list of your references using a reference manager or similar system, with tags so that you can easily identify the source of your information.

Final tips for writing and structuring a science thesis/dissertation

Writing a thesis/dissertation is a rewarding process. As a final tip for getting through this process successfully, don’t forget to leave sufficient time for editing and proofreading. Your thesis/dissertation will go through many drafts and revisions before it reaches its final form.

Engaging the services of a professional can go a long way in helping you produce a professional and high-quality document worthy of your research. In addition, there are many helpful tools like AI grammar checker tools available online for students and young researchers.

Check out our site for more tips on how to write a good thesis/dissertation , where to find the best thesis editing services , and more about thesis editing and proofreading services .

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Review Checklist

Use this checklist to ensure that your science thesis/dissertation isn’t missing any important structural components.

Title page: Does your thesis/dissertation have a title page with your title, name, department, advisor’s name, and other important information?

Acknowledgements: Did you give credit to your funders, research colleagues, and anyone else who helped you?

Abstract: Does your thesis/dissertation include a brief summary?

Table of contents: Does your table of contents include headings, subheadings, and page numbers?

Figure and tables: Is there a complete list of figures and tables that are in your thesis/dissertation?

List of abbreviations: Are all of the abbreviations used in your thesis/dissertation listed here?

Glossary: Did you clearly define any specialized terminology used in your thesis/dissertation?

Introduction/Literature review: Did you justify your research study, state your objectives, and your hypothesis? Did you review the previous relevant literature in your field and explain how your thesis/dissertation fits in?

Materials/Methodology: Could another scientist replicate what you did by reading this section?

Results: Did you include all of the data from your experiments/research study?

Discussion/Conclusion: Did you clearly explain what your results mean and whether your hypothesis was correct or not?

Reference list: Are your references properly formatted and listed alphabetically or numerically?

Bibliography and Appendices: Did you include any additional relevant data, figures, or text that didn’t fit into the main section of your thesis/dissertation?

How long is a typical science thesis/dissertation? +

A typical Master’s thesis/dissertation ranges from 15,000-45,000 words, while a Ph.D. thesis/dissertation can be as much as 80,000 words.

How do I start my thesis/dissertation? +

You don’t have to start with the introduction when you begin writing. You can start with the methodology section or any other section you prefer and revise it later.

How do I structure a science thesis/dissertation? +

The main section of a science thesis/dissertation includes an introduction/literature review, materials/methodology section, results, discussion/conclusion section, and a references list.

While Sandel argues that pursuing perfection through genetic engineering would decrease our sense of humility, he claims that the sense of solidarity we would lose is also important.

This thesis summarizes several points in Sandel’s argument, but it does not make a claim about how we should understand his argument. A reader who read Sandel’s argument would not also need to read an essay based on this descriptive thesis.  

Broad thesis (arguable, but difficult to support with evidence) 

Michael Sandel’s arguments about genetic engineering do not take into consideration all the relevant issues.

This is an arguable claim because it would be possible to argue against it by saying that Michael Sandel’s arguments do take all of the relevant issues into consideration. But the claim is too broad. Because the thesis does not specify which “issues” it is focused on—or why it matters if they are considered—readers won’t know what the rest of the essay will argue, and the writer won’t know what to focus on. If there is a particular issue that Sandel does not address, then a more specific version of the thesis would include that issue—hand an explanation of why it is important.  

Arguable thesis with analytical claim 

While Sandel argues persuasively that our instinct to “remake” (54) ourselves into something ever more perfect is a problem, his belief that we can always draw a line between what is medically necessary and what makes us simply “better than well” (51) is less convincing.

This is an arguable analytical claim. To argue for this claim, the essay writer will need to show how evidence from the article itself points to this interpretation. It’s also a reasonable scope for a thesis because it can be supported with evidence available in the text and is neither too broad nor too narrow.  

Arguable thesis with normative claim 

Given Sandel’s argument against genetic enhancement, we should not allow parents to decide on using Human Growth Hormone for their children.

This thesis tells us what we should do about a particular issue discussed in Sandel’s article, but it does not tell us how we should understand Sandel’s argument.  

Questions to ask about your thesis 

• Is the thesis truly arguable? Does it speak to a genuine dilemma in the source, or would most readers automatically agree with it?  
• Is the thesis too obvious? Again, would most or all readers agree with it without needing to see your argument?  
• Is the thesis complex enough to require a whole essay's worth of argument?  
• Is the thesis supportable with evidence from the text rather than with generalizations or outside research?  
• Would anyone want to read a paper in which this thesis was developed? That is, can you explain what this paper is adding to our understanding of a problem, question, or topic?
• picture_as_pdf Thesis

What Is a Thesis?

• First Online: 01 January 2014

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• David Evans† 4 ,
• Paul Gruba 5 &
• Justin Zobel 6  

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Simply defined, a thesis is an extended argument. To pass, a thesis must demonstrate logical, structured, and defensible reasoning based on credible and verifiable evidence presented in such a way that it makes an original contribution to knowledge, as judged by experts in the field. Among the many types of scholarly productions, theses are an oddity: each one is different, and there are no standard or generic constructions. Most of those who supervise theses have written just one, and, despite the effort they take to produce, the only people who carefully read a given thesis are the project supervisors, the examiners, and an otherwise rather select audience of specialized academics.

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Evans†, D., Gruba, P., Zobel, J. (2014). What Is a Thesis?. In: How to Write a Better Thesis. Springer, Cham. https://doi.org/10.1007/978-3-319-04286-2_1

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• 1 Anatomy Department, University of Malta, Malta. Electronic address: [email protected].
• 2 University of Malta and Consultant Paediatric Cardiologist, Mater Dei Hospital, Malta. Electronic address: [email protected].
• 3 Department of Obstetrics and Gynaecology, with the Faculty of Medicine & Surgery, University of Malta, Malta. Electronic address: [email protected].
• PMID: 30086984
• DOI: 10.1016/j.earlhumdev.2018.07.012

The prospect of writing a thesis is considered daunting by many but the task is a requisite when embarking into reading for any academic degree. A thesis is a written document following personal research. It is performed to obtain an academic degree or qualification, both at undergraduate and postgraduate level. When writing a thesis, it is imperative that the student follows the universal well-acknowledged structure format known as "IMRAD": i.e. Introduction, Methods, Results and Discussion. A summary of the thesis, known as the 'Abstract', is placed at the beginning of the "IMRAD", while all references cited in the thesis are placed at the very end. The thesis format is similar to a research manuscript prepared for publication in scientific journal, but there are significant differences between the two types of academic works. For example, the liberal use of graphical aides in the form of figures and/or tables enhances the delivery of results. It is essential to ensure that all the literature referred to in the thesis is cited, while paying particular attention to potential plagiarism. When writing a thesis, the student needs to keep in mind three factors: [1] the structure, [2] the substance and [3] the style. Once the student has developed a good plan for thesis layout, then writing becomes greatly faciliated.

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• Academic Dissertations as Topic*
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"Scientific research is one of the most exciting and rewarding of occupations."

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dissertation tips for curious researchers

Writing a Science Thesis: Tips to Make Your Path Easier

Academic papers and research projects are among the tasks that play a significant role in the overall grades of a student. While such academic papers or tasks may come in the form of essays, research projects, coursework, and scientific papers, they have one thing in common; an approved structure and formatting guidelines.

Crafting a perfect science thesis paper can be hard, and that is why, in this guide, we explore thesis tips for science and technology writing. Keep reading for a better understanding to know what to do when writing for science.

What is a scientific thesis?

A science thesis is a science research paper in which the student performs original research on a science topic and submits the paper as the final step of the master’s program. Science writing is quite a complex and daunting task that requires adequate knowledge of the subject matter. Besides, it is among the longest academic papers students have to write as part of their career progress.

Importance of technical writing in science

Technical writing is all about communicating technical or complex information on a given field in simplified language to make it easy for the readers to understand the context.

Scientific writing, on the other hand, is all about conveying scientific information in an easy-to-interpret manner.

Therefore, technical writing in science is important as it helps to interpret complex science phrases or terms using different illustrations through diagrams and illustrations. This means that technical writers must understand the working of computers well to create varied programs that will help them convey scientific information in a simplified version.

Science writing tips

When tasked with a scientific thesis task and don’t know how to go about everything, try the tips highlighted below to have an easy time doing the research and compiling the information to bring out a meaningful paper.

1. Select the best science thesis topics

An incredible science thesis begins with a compelling thesis title related to science. Therefore, before you begin the research process, have several topics, then filter out a perfect topic to fit the subject you want to research. Besides, ensure the final topic you settle on is not only interesting to research but also interesting to read.

2. Create a science thesis timeline appropriately

Based on the timeframe you have, try and figure out if the materials and resources you have will help you achieve your goal. Above all, do an analysis and figure out of the whole project will require external funding. Once you have an appropriate timeline, you can adjust it as often as you want as long as you finish the project successfully and on time.

3. Don’t wait for the last minute

Unlike other common essays and some academic papers, a science thesis is a long project that will require you to invest your time well. Therefore, as you do the research, make sure you write the draft as well. Once you are done with the research, you will have a good draft you can refer to when writing the final paper for submission.  

4. Save or keep your reference list

Of course, you will need to cite your work once you have finished the research. Besides, you might need to review some points for further understanding. That is why it is significant to keep track of all your reference materials for easy tracing and to have an easy time writing your paper.

5. Get professional help

It is common for students not to have adequate time to do the research while meeting other activities. Besides, it can be a complex topic that a student cannot comprehend well. As a student, in such instances, you can opt for professionals who help with everything to do with a science thesis and have your paper done perfectly and on time.

Are you almost writing for science? Well, put the above concepts into action, and you will never struggle with your paper.

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How to Write a Thesis or Dissertation Introduction

Published on September 7, 2022 by Tegan George and Shona McCombes. Revised on November 21, 2023.

The introduction is the first section of your thesis or dissertation , appearing right after the table of contents . Your introduction draws your reader in, setting the stage for your research with a clear focus, purpose, and direction on a relevant topic .

Your introduction should include:

• Your topic, in context: what does your reader need to know to understand your thesis dissertation?
• Your focus and scope: what specific aspect of the topic will you address?
• The relevance of your research: how does your work fit into existing studies on your topic?
• Your questions and objectives: what does your research aim to find out, and how?
• An overview of your structure: what does each section contribute to the overall aim?

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Table of contents

How to start your introduction, topic and context, focus and scope, relevance and importance, questions and objectives, overview of the structure, thesis introduction example, introduction checklist, other interesting articles, frequently asked questions about introductions.

Although your introduction kicks off your dissertation, it doesn’t have to be the first thing you write — in fact, it’s often one of the very last parts to be completed (just before your abstract ).

It’s a good idea to write a rough draft of your introduction as you begin your research, to help guide you. If you wrote a research proposal , consider using this as a template, as it contains many of the same elements. However, be sure to revise your introduction throughout the writing process, making sure it matches the content of your ensuing sections.

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Begin by introducing your dissertation topic and giving any necessary background information. It’s important to contextualize your research and generate interest. Aim to show why your topic is timely or important. You may want to mention a relevant news item, academic debate, or practical problem.

After a brief introduction to your general area of interest, narrow your focus and define the scope of your research.

You can narrow this down in many ways, such as by:

• Geographical area
• Time period
• Demographics or communities
• Themes or aspects of the topic

It’s essential to share your motivation for doing this research, as well as how it relates to existing work on your topic. Further, you should also mention what new insights you expect it will contribute.

Start by giving a brief overview of the current state of research. You should definitely cite the most relevant literature, but remember that you will conduct a more in-depth survey of relevant sources in the literature review section, so there’s no need to go too in-depth in the introduction.

Depending on your field, the importance of your research might focus on its practical application (e.g., in policy or management) or on advancing scholarly understanding of the topic (e.g., by developing theories or adding new empirical data). In many cases, it will do both.

Ultimately, your introduction should explain how your thesis or dissertation:

• Helps solve a practical or theoretical problem
• Addresses a gap in the literature
• Builds on existing research
• Proposes a new understanding of your topic

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Perhaps the most important part of your introduction is your questions and objectives, as it sets up the expectations for the rest of your thesis or dissertation. How you formulate your research questions and research objectives will depend on your discipline, topic, and focus, but you should always clearly state the central aim of your research.

If your research aims to test hypotheses , you can formulate them here. Your introduction is also a good place for a conceptual framework that suggests relationships between variables .

• Conduct surveys to collect data on students’ levels of knowledge, understanding, and positive/negative perceptions of government policy.
• Determine whether attitudes to climate policy are associated with variables such as age, gender, region, and social class.
• Conduct interviews to gain qualitative insights into students’ perspectives and actions in relation to climate policy.

To help guide your reader, end your introduction with an outline  of the structure of the thesis or dissertation to follow. Share a brief summary of each chapter, clearly showing how each contributes to your central aims. However, be careful to keep this overview concise: 1-2 sentences should be enough.

I. Introduction

Human language consists of a set of vowels and consonants which are combined to form words. During the speech production process, thoughts are converted into spoken utterances to convey a message. The appropriate words and their meanings are selected in the mental lexicon (Dell & Burger, 1997). This pre-verbal message is then grammatically coded, during which a syntactic representation of the utterance is built.

Speech, language, and voice disorders affect the vocal cords, nerves, muscles, and brain structures, which result in a distorted language reception or speech production (Sataloff & Hawkshaw, 2014). The symptoms vary from adding superfluous words and taking pauses to hoarseness of the voice, depending on the type of disorder (Dodd, 2005). However, distortions of the speech may also occur as a result of a disease that seems unrelated to speech, such as multiple sclerosis or chronic obstructive pulmonary disease.

This study aims to determine which acoustic parameters are suitable for the automatic detection of exacerbations in patients suffering from chronic obstructive pulmonary disease (COPD) by investigating which aspects of speech differ between COPD patients and healthy speakers and which aspects differ between COPD patients in exacerbation and stable COPD patients.

Checklist: Introduction

I have introduced my research topic in an engaging way.

I have provided necessary context to help the reader understand my topic.

I have clearly specified the focus of my research.

I have shown the relevance and importance of the dissertation topic .

I have clearly stated the problem or question that my research addresses.

I have outlined the specific objectives of the research .

I have provided an overview of the dissertation’s structure .

You've written a strong introduction for your thesis or dissertation. Use the other checklists to continue improving your dissertation.

If you want to know more about AI for academic writing, AI tools, or research bias, make sure to check out some of our other articles with explanations and examples or go directly to our tools!

Research bias

• Survivorship bias
• Self-serving bias
• Availability heuristic
• Halo effect
• Hindsight bias
• Deep learning
• Generative AI
• Machine learning
• Reinforcement learning
• Supervised vs. unsupervised learning

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The introduction of a research paper includes several key elements:

• A hook to catch the reader’s interest
• Relevant background on the topic
• Details of your research problem

and your problem statement

• A thesis statement or research question
• Sometimes an overview of the paper

Don’t feel that you have to write the introduction first. The introduction is often one of the last parts of the research paper you’ll write, along with the conclusion.

This is because it can be easier to introduce your paper once you’ve already written the body ; you may not have the clearest idea of your arguments until you’ve written them, and things can change during the writing process .

Research objectives describe what you intend your research project to accomplish.

They summarize the approach and purpose of the project and help to focus your research.

Your objectives should appear in the introduction of your research paper , at the end of your problem statement .

Scope of research is determined at the beginning of your research process , prior to the data collection stage. Sometimes called “scope of study,” your scope delineates what will and will not be covered in your project. It helps you focus your work and your time, ensuring that you’ll be able to achieve your goals and outcomes.

Defining a scope can be very useful in any research project, from a research proposal to a thesis or dissertation . A scope is needed for all types of research: quantitative , qualitative , and mixed methods .

To define your scope of research, consider the following:

• Budget constraints or any specifics of grant funding
• Your proposed timeline and duration
• Specifics about your population of study, your proposed sample size , and the research methodology you’ll pursue
• Any inclusion and exclusion criteria
• Any anticipated control , extraneous , or confounding variables that could bias your research if not accounted for properly.

Cite this Scribbr article

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George, T. & McCombes, S. (2023, November 21). How to Write a Thesis or Dissertation Introduction. Scribbr. Retrieved April 9, 2024, from https://www.scribbr.com/dissertation/introduction-structure/

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Definition of thesis noun from the Oxford Advanced Learner's Dictionary

• Students must submit a thesis on an agreed subject within four years.
• He presented this thesis for his PhD.
• a thesis for a master's degree
• He's doing a doctoral thesis on the early works of Shostakovich.
• Many departments require their students to do a thesis defense.
• She completed an MSc by thesis.
• her thesis adviser at MIT
• in a/​the thesis
• thesis about

Questions about grammar and vocabulary?

Find the answers with Practical English Usage online, your indispensable guide to problems in English.

• The basic thesis of the book is fairly simple.
• These latest findings support the thesis that sexuality is determined by nature rather than choice.
• formulate/​advance a theory/​hypothesis
• build/​construct/​create/​develop a simple/​theoretical/​mathematical model
• develop/​establish/​provide/​use a theoretical/​conceptual framework
• advance/​argue/​develop the thesis that…
• explore an idea/​a concept/​a hypothesis
• make a prediction/​an inference
• base a prediction/​your calculations on something
• investigate/​evaluate/​accept/​challenge/​reject a theory/​hypothesis/​model
• design an experiment/​a questionnaire/​a study/​a test
• do research/​an experiment/​an analysis
• make observations/​measurements/​calculations
• carry out/​conduct/​perform an experiment/​a test/​a longitudinal study/​observations/​clinical trials
• run an experiment/​a simulation/​clinical trials
• repeat an experiment/​a test/​an analysis
• replicate a study/​the results/​the findings
• observe/​study/​examine/​investigate/​assess a pattern/​a process/​a behaviour
• fund/​support the research/​project/​study
• seek/​provide/​get/​secure funding for research
• collect/​gather/​extract data/​information
• yield data/​evidence/​similar findings/​the same results
• analyse/​examine the data/​soil samples/​a specimen
• consider/​compare/​interpret the results/​findings
• fit the data/​model
• confirm/​support/​verify a prediction/​a hypothesis/​the results/​the findings
• prove a conjecture/​hypothesis/​theorem
• draw/​make/​reach the same conclusions
• read/​review the records/​literature
• describe/​report an experiment/​a study
• present/​publish/​summarize the results/​findings
• present/​publish/​read/​review/​cite a paper in a scientific journal
• The results of the experiment support his central thesis.
• Most people rejected this thesis at the time because it presumed evolution rather than creation.
• fundamental

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What Is A Research (Scientific) Hypothesis? A plain-language explainer + examples

By:  Derek Jansen (MBA)  | Reviewed By: Dr Eunice Rautenbach | June 2020

If you’re new to the world of research, or it’s your first time writing a dissertation or thesis, you’re probably noticing that the words “research hypothesis” and “scientific hypothesis” are used quite a bit, and you’re wondering what they mean in a research context .

“Hypothesis” is one of those words that people use loosely, thinking they understand what it means. However, it has a very specific meaning within academic research. So, it’s important to understand the exact meaning before you start hypothesizing. 

Research Hypothesis 101

• What is a hypothesis ?
• What is a research hypothesis (scientific hypothesis)?
• Requirements for a research hypothesis
• Definition of a research hypothesis
• The null hypothesis

What is a hypothesis?

Let’s start with the general definition of a hypothesis (not a research hypothesis or scientific hypothesis), according to the Cambridge Dictionary:

Hypothesis: an idea or explanation for something that is based on known facts but has not yet been proved.

In other words, it’s a statement that provides an explanation for why or how something works, based on facts (or some reasonable assumptions), but that has not yet been specifically tested . For example, a hypothesis might look something like this:

Hypothesis: sleep impacts academic performance.

This statement predicts that academic performance will be influenced by the amount and/or quality of sleep a student engages in – sounds reasonable, right? It’s based on reasonable assumptions , underpinned by what we currently know about sleep and health (from the existing literature). So, loosely speaking, we could call it a hypothesis, at least by the dictionary definition.

But that’s not good enough…

Unfortunately, that’s not quite sophisticated enough to describe a research hypothesis (also sometimes called a scientific hypothesis), and it wouldn’t be acceptable in a dissertation, thesis or research paper . In the world of academic research, a statement needs a few more criteria to constitute a true research hypothesis .

What is a research hypothesis?

A research hypothesis (also called a scientific hypothesis) is a statement about the expected outcome of a study (for example, a dissertation or thesis). To constitute a quality hypothesis, the statement needs to have three attributes – specificity , clarity and testability .

Let’s take a look at these more closely.

Need a helping hand?

Hypothesis Essential #1: Specificity & Clarity

A good research hypothesis needs to be extremely clear and articulate about both what’ s being assessed (who or what variables are involved ) and the expected outcome (for example, a difference between groups, a relationship between variables, etc.).

Let’s stick with our sleepy students example and look at how this statement could be more specific and clear.

Hypothesis: Students who sleep at least 8 hours per night will, on average, achieve higher grades in standardised tests than students who sleep less than 8 hours a night.

As you can see, the statement is very specific as it identifies the variables involved (sleep hours and test grades), the parties involved (two groups of students), as well as the predicted relationship type (a positive relationship). There’s no ambiguity or uncertainty about who or what is involved in the statement, and the expected outcome is clear.

Contrast that to the original hypothesis we looked at – “Sleep impacts academic performance” – and you can see the difference. “Sleep” and “academic performance” are both comparatively vague , and there’s no indication of what the expected relationship direction is (more sleep or less sleep). As you can see, specificity and clarity are key.

Hypothesis Essential #2: Testability (Provability)

A statement must be testable to qualify as a research hypothesis. In other words, there needs to be a way to prove (or disprove) the statement. If it’s not testable, it’s not a hypothesis – simple as that.

For example, consider the hypothesis we mentioned earlier:

Hypothesis: Students who sleep at least 8 hours per night will, on average, achieve higher grades in standardised tests than students who sleep less than 8 hours a night.  

We could test this statement by undertaking a quantitative study involving two groups of students, one that gets 8 or more hours of sleep per night for a fixed period, and one that gets less. We could then compare the standardised test results for both groups to see if there’s a statistically significant difference. 

Again, if you compare this to the original hypothesis we looked at – “Sleep impacts academic performance” – you can see that it would be quite difficult to test that statement, primarily because it isn’t specific enough. How much sleep? By who? What type of academic performance?

So, remember the mantra – if you can’t test it, it’s not a hypothesis 🙂

Defining A Research Hypothesis

You’re still with us? Great! Let’s recap and pin down a clear definition of a hypothesis.

A research hypothesis (or scientific hypothesis) is a statement about an expected relationship between variables, or explanation of an occurrence, that is clear, specific and testable.

So, when you write up hypotheses for your dissertation or thesis, make sure that they meet all these criteria. If you do, you’ll not only have rock-solid hypotheses but you’ll also ensure a clear focus for your entire research project.

What about the null hypothesis?

You may have also heard the terms null hypothesis , alternative hypothesis, or H-zero thrown around. At a simple level, the null hypothesis is the counter-proposal to the original hypothesis.

For example, if the hypothesis predicts that there is a relationship between two variables (for example, sleep and academic performance), the null hypothesis would predict that there is no relationship between those variables.

At a more technical level, the null hypothesis proposes that no statistical significance exists in a set of given observations and that any differences are due to chance alone.

And there you have it – hypotheses in a nutshell. 

If you have any questions, be sure to leave a comment below and we’ll do our best to help you. If you need hands-on help developing and testing your hypotheses, consider our private coaching service , where we hold your hand through the research journey.

Psst… there’s more (for free)

This post is part of our dissertation mini-course, which covers everything you need to get started with your dissertation, thesis or research project. 

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16 Comments

Very useful information. I benefit more from getting more information in this regard.

Very great insight,educative and informative. Please give meet deep critics on many research data of public international Law like human rights, environment, natural resources, law of the sea etc

In a book I read a distinction is made between null, research, and alternative hypothesis. As far as I understand, alternative and research hypotheses are the same. Can you please elaborate? Best Afshin

This is a self explanatory, easy going site. I will recommend this to my friends and colleagues.

Very good definition. How can I cite your definition in my thesis? Thank you. Is nul hypothesis compulsory in a research?

It’s a counter-proposal to be proven as a rejection

Please what is the difference between alternate hypothesis and research hypothesis?

It is a very good explanation. However, it limits hypotheses to statistically tasteable ideas. What about for qualitative researches or other researches that involve quantitative data that don’t need statistical tests?

In qualitative research, one typically uses propositions, not hypotheses.

could you please elaborate it more

I’ve benefited greatly from these notes, thank you.

This is very helpful

well articulated ideas are presented here, thank you for being reliable sources of information

Excellent. Thanks for being clear and sound about the research methodology and hypothesis (quantitative research)

I have only a simple question regarding the null hypothesis. – Is the null hypothesis (Ho) known as the reversible hypothesis of the alternative hypothesis (H1? – How to test it in academic research?

this is very important note help me much more

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What is Scientific Research and How Can it be Done?

Scientific researches are studies that should be systematically planned before performing them. In this review, classification and description of scientific studies, planning stage randomisation and bias are explained.

Research conducted for the purpose of contributing towards science by the systematic collection, interpretation and evaluation of data and that, too, in a planned manner is called scientific research: a researcher is the one who conducts this research. The results obtained from a small group through scientific studies are socialised, and new information is revealed with respect to diagnosis, treatment and reliability of applications. The purpose of this review is to provide information about the definition, classification and methodology of scientific research.

Before beginning the scientific research, the researcher should determine the subject, do planning and specify the methodology. In the Declaration of Helsinki, it is stated that ‘the primary purpose of medical researches on volunteers is to understand the reasons, development and effects of diseases and develop protective, diagnostic and therapeutic interventions (method, operation and therapies). Even the best proven interventions should be evaluated continuously by investigations with regard to reliability, effectiveness, efficiency, accessibility and quality’ ( 1 ).

The questions, methods of response to questions and difficulties in scientific research may vary, but the design and structure are generally the same ( 2 ).

Classification of Scientific Research

Scientific research can be classified in several ways. Classification can be made according to the data collection techniques based on causality, relationship with time and the medium through which they are applied.

• Observational
• Experimental
• Descriptive
• Retrospective
• Prospective
• Cross-sectional
• Social descriptive research ( 3 )

Another method is to classify the research according to its descriptive or analytical features. This review is written according to this classification method.

I. Descriptive research

• Case series
• Surveillance studies

II. Analytical research

• Observational studies: cohort, case control and cross- sectional research
• Interventional research: quasi-experimental and clinical research
• Case Report: it is the most common type of descriptive study. It is the examination of a single case having a different quality in the society, e.g. conducting general anaesthesia in a pregnant patient with mucopolysaccharidosis.
• Case Series: it is the description of repetitive cases having common features. For instance; case series involving interscapular pain related to neuraxial labour analgesia. Interestingly, malignant hyperthermia cases are not accepted as case series since they are rarely seen during historical development.
• Surveillance Studies: these are the results obtained from the databases that follow and record a health problem for a certain time, e.g. the surveillance of cross-infections during anaesthesia in the intensive care unit.

Moreover, some studies may be experimental. After the researcher intervenes, the researcher waits for the result, observes and obtains data. Experimental studies are, more often, in the form of clinical trials or laboratory animal trials ( 2 ).

Analytical observational research can be classified as cohort, case-control and cross-sectional studies.

Firstly, the participants are controlled with regard to the disease under investigation. Patients are excluded from the study. Healthy participants are evaluated with regard to the exposure to the effect. Then, the group (cohort) is followed-up for a sufficient period of time with respect to the occurrence of disease, and the progress of disease is studied. The risk of the healthy participants getting sick is considered an incident. In cohort studies, the risk of disease between the groups exposed and not exposed to the effect is calculated and rated. This rate is called relative risk. Relative risk indicates the strength of exposure to the effect on the disease.

Cohort research may be observational and experimental. The follow-up of patients prospectively is called a prospective cohort study . The results are obtained after the research starts. The researcher’s following-up of cohort subjects from a certain point towards the past is called a retrospective cohort study . Prospective cohort studies are more valuable than retrospective cohort studies: this is because in the former, the researcher observes and records the data. The researcher plans the study before the research and determines what data will be used. On the other hand, in retrospective studies, the research is made on recorded data: no new data can be added.

In fact, retrospective and prospective studies are not observational. They determine the relationship between the date on which the researcher has begun the study and the disease development period. The most critical disadvantage of this type of research is that if the follow-up period is long, participants may leave the study at their own behest or due to physical conditions. Cohort studies that begin after exposure and before disease development are called ambidirectional studies . Public healthcare studies generally fall within this group, e.g. lung cancer development in smokers.

• Case-Control Studies: these studies are retrospective cohort studies. They examine the cause and effect relationship from the effect to the cause. The detection or determination of data depends on the information recorded in the past. The researcher has no control over the data ( 2 ).

Cross-sectional studies are advantageous since they can be concluded relatively quickly. It may be difficult to obtain a reliable result from such studies for rare diseases ( 2 ).

Cross-sectional studies are characterised by timing. In such studies, the exposure and result are simultaneously evaluated. While cross-sectional studies are restrictedly used in studies involving anaesthesia (since the process of exposure is limited), they can be used in studies conducted in intensive care units.

• Quasi-Experimental Research: they are conducted in cases in which a quick result is requested and the participants or research areas cannot be randomised, e.g. giving hand-wash training and comparing the frequency of nosocomial infections before and after hand wash.
• Clinical Research: they are prospective studies carried out with a control group for the purpose of comparing the effect and value of an intervention in a clinical case. Clinical study and research have the same meaning. Drugs, invasive interventions, medical devices and operations, diets, physical therapy and diagnostic tools are relevant in this context ( 6 ).

Clinical studies are conducted by a responsible researcher, generally a physician. In the research team, there may be other healthcare staff besides physicians. Clinical studies may be financed by healthcare institutes, drug companies, academic medical centres, volunteer groups, physicians, healthcare service providers and other individuals. They may be conducted in several places including hospitals, universities, physicians’ offices and community clinics based on the researcher’s requirements. The participants are made aware of the duration of the study before their inclusion. Clinical studies should include the evaluation of recommendations (drug, device and surgical) for the treatment of a disease, syndrome or a comparison of one or more applications; finding different ways for recognition of a disease or case and prevention of their recurrence ( 7 ).

Clinical Research

In this review, clinical research is explained in more detail since it is the most valuable study in scientific research.

Clinical research starts with forming a hypothesis. A hypothesis can be defined as a claim put forward about the value of a population parameter based on sampling. There are two types of hypotheses in statistics.

• H 0 hypothesis is called a control or null hypothesis. It is the hypothesis put forward in research, which implies that there is no difference between the groups under consideration. If this hypothesis is rejected at the end of the study, it indicates that a difference exists between the two treatments under consideration.
• H 1 hypothesis is called an alternative hypothesis. It is hypothesised against a null hypothesis, which implies that a difference exists between the groups under consideration. For example, consider the following hypothesis: drug A has an analgesic effect. Control or null hypothesis (H 0 ): there is no difference between drug A and placebo with regard to the analgesic effect. The alternative hypothesis (H 1 ) is applicable if a difference exists between drug A and placebo with regard to the analgesic effect.

The planning phase comes after the determination of a hypothesis. A clinical research plan is called a protocol . In a protocol, the reasons for research, number and qualities of participants, tests to be applied, study duration and what information to be gathered from the participants should be found and conformity criteria should be developed.

The selection of participant groups to be included in the study is important. Inclusion and exclusion criteria of the study for the participants should be determined. Inclusion criteria should be defined in the form of demographic characteristics (age, gender, etc.) of the participant group and the exclusion criteria as the diseases that may influence the study, age ranges, cases involving pregnancy and lactation, continuously used drugs and participants’ cooperation.

The next stage is methodology. Methodology can be grouped under subheadings, namely, the calculation of number of subjects, blinding (masking), randomisation, selection of operation to be applied, use of placebo and criteria for stopping and changing the treatment.

I. Calculation of the Number of Subjects

The entire source from which the data are obtained is called a universe or population . A small group selected from a certain universe based on certain rules and which is accepted to highly represent the universe from which it is selected is called a sample and the characteristics of the population from which the data are collected are called variables. If data is collected from the entire population, such an instance is called a parameter . Conducting a study on the sample rather than the entire population is easier and less costly. Many factors influence the determination of the sample size. Firstly, the type of variable should be determined. Variables are classified as categorical (qualitative, non-numerical) or numerical (quantitative). Individuals in categorical variables are classified according to their characteristics. Categorical variables are indicated as nominal and ordinal (ordered). In nominal variables, the application of a category depends on the researcher’s preference. For instance, a female participant can be considered first and then the male participant, or vice versa. An ordinal (ordered) variable is ordered from small to large or vice versa (e.g. ordering obese patients based on their weights-from the lightest to the heaviest or vice versa). A categorical variable may have more than one characteristic: such variables are called binary or dichotomous (e.g. a participant may be both female and obese).

If the variable has numerical (quantitative) characteristics and these characteristics cannot be categorised, then it is called a numerical variable. Numerical variables are either discrete or continuous. For example, the number of operations with spinal anaesthesia represents a discrete variable. The haemoglobin value or height represents a continuous variable.

Statistical analyses that need to be employed depend on the type of variable. The determination of variables is necessary for selecting the statistical method as well as software in SPSS. While categorical variables are presented as numbers and percentages, numerical variables are represented using measures such as mean and standard deviation. It may be necessary to use mean in categorising some cases such as the following: even though the variable is categorical (qualitative, non-numerical) when Visual Analogue Scale (VAS) is used (since a numerical value is obtained), it is classified as a numerical variable: such variables are averaged.

Clinical research is carried out on the sample and generalised to the population. Accordingly, the number of samples should be correctly determined. Different sample size formulas are used on the basis of the statistical method to be used. When the sample size increases, error probability decreases. The sample size is calculated based on the primary hypothesis. The determination of a sample size before beginning the research specifies the power of the study. Power analysis enables the acquisition of realistic results in the research, and it is used for comparing two or more clinical research methods.

Because of the difference in the formulas used in calculating power analysis and number of samples for clinical research, it facilitates the use of computer programs for making calculations.

It is necessary to know certain parameters in order to calculate the number of samples by power analysis.

• Type-I (α) and type-II (β) error levels
• Difference between groups (d-difference) and effect size (ES)
• Distribution ratio of groups
• Direction of research hypothesis (H1)

a. Type-I (α) and Type-II (β) Error (β) Levels

Two types of errors can be made while accepting or rejecting H 0 hypothesis in a hypothesis test. Type-I error (α) level is the probability of finding a difference at the end of the research when there is no difference between the two applications. In other words, it is the rejection of the hypothesis when H 0 is actually correct and it is known as α error or p value. For instance, when the size is determined, type-I error level is accepted as 0.05 or 0.01.

Another error that can be made during a hypothesis test is a type-II error. It is the acceptance of a wrongly hypothesised H 0 hypothesis. In fact, it is the probability of failing to find a difference when there is a difference between the two applications. The power of a test is the ability of that test to find a difference that actually exists. Therefore, it is related to the type-II error level.

Since the type-II error risk is expressed as β, the power of the test is defined as 1–β. When a type-II error is 0.20, the power of the test is 0.80. Type-I (α) and type-II (β) errors can be intentional. The reason to intentionally make such an error is the necessity to look at the events from the opposite perspective.

b. Difference between Groups and ES

ES is defined as the state in which statistical difference also has clinically significance: ES≥0.5 is desirable. The difference between groups is the absolute difference between the groups compared in clinical research.

c. Allocation Ratio of Groups

The allocation ratio of groups is effective in determining the number of samples. If the number of samples is desired to be determined at the lowest level, the rate should be kept as 1/1.

d. Direction of Hypothesis (H1)

The direction of hypothesis in clinical research may be one-sided or two-sided. While one-sided hypotheses hypothesis test differences in the direction of size, two-sided hypotheses hypothesis test differences without direction. The power of the test in two-sided hypotheses is lower than one-sided hypotheses.

After these four variables are determined, they are entered in the appropriate computer program and the number of samples is calculated. Statistical packaged software programs such as Statistica, NCSS and G-Power may be used for power analysis and calculating the number of samples. When the samples size is calculated, if there is a decrease in α, difference between groups, ES and number of samples, then the standard deviation increases and power decreases. The power in two-sided hypothesis is lower. It is ethically appropriate to consider the determination of sample size, particularly in animal experiments, at the beginning of the study. The phase of the study is also important in the determination of number of subjects to be included in drug studies. Usually, phase-I studies are used to determine the safety profile of a drug or product, and they are generally conducted on a few healthy volunteers. If no unacceptable toxicity is detected during phase-I studies, phase-II studies may be carried out. Phase-II studies are proof-of-concept studies conducted on a larger number (100–500) of volunteer patients. When the effectiveness of the drug or product is evident in phase-II studies, phase-III studies can be initiated. These are randomised, double-blinded, placebo or standard treatment-controlled studies. Volunteer patients are periodically followed-up with respect to the effectiveness and side effects of the drug. It can generally last 1–4 years and is valuable during licensing and releasing the drug to the general market. Then, phase-IV studies begin in which long-term safety is investigated (indication, dose, mode of application, safety, effectiveness, etc.) on thousands of volunteer patients.

II. Blinding (Masking) and Randomisation Methods

When the methodology of clinical research is prepared, precautions should be taken to prevent taking sides. For this reason, techniques such as randomisation and blinding (masking) are used. Comparative studies are the most ideal ones in clinical research.

Blinding Method

A case in which the treatments applied to participants of clinical research should be kept unknown is called the blinding method . If the participant does not know what it receives, it is called a single-blind study; if even the researcher does not know, it is called a double-blind study. When there is a probability of knowing which drug is given in the order of application, when uninformed staff administers the drug, it is called in-house blinding. In case the study drug is known in its pharmaceutical form, a double-dummy blinding test is conducted. Intravenous drug is given to one group and a placebo tablet is given to the comparison group; then, the placebo tablet is given to the group that received the intravenous drug and intravenous drug in addition to placebo tablet is given to the comparison group. In this manner, each group receives both the intravenous and tablet forms of the drug. In case a third party interested in the study is involved and it also does not know about the drug (along with the statistician), it is called third-party blinding.

Randomisation Method

The selection of patients for the study groups should be random. Randomisation methods are used for such selection, which prevent conscious or unconscious manipulations in the selection of patients ( 8 ).

No factor pertaining to the patient should provide preference of one treatment to the other during randomisation. This characteristic is the most important difference separating randomised clinical studies from prospective and synchronous studies with experimental groups. Randomisation strengthens the study design and enables the determination of reliable scientific knowledge ( 2 ).

The easiest method is simple randomisation, e.g. determination of the type of anaesthesia to be administered to a patient by tossing a coin. In this method, when the number of samples is kept high, a balanced distribution is created. When the number of samples is low, there will be an imbalance between the groups. In this case, stratification and blocking have to be added to randomisation. Stratification is the classification of patients one or more times according to prognostic features determined by the researcher and blocking is the selection of a certain number of patients for each stratification process. The number of stratification processes should be determined at the beginning of the study.

As the number of stratification processes increases, performing the study and balancing the groups become difficult. For this reason, stratification characteristics and limitations should be effectively determined at the beginning of the study. It is not mandatory for the stratifications to have equal intervals. Despite all the precautions, an imbalance might occur between the groups before beginning the research. In such circumstances, post-stratification or restandardisation may be conducted according to the prognostic factors.

The main characteristic of applying blinding (masking) and randomisation is the prevention of bias. Therefore, it is worthwhile to comprehensively examine bias at this stage.

Bias and Chicanery

While conducting clinical research, errors can be introduced voluntarily or involuntarily at a number of stages, such as design, population selection, calculating the number of samples, non-compliance with study protocol, data entry and selection of statistical method. Bias is taking sides of individuals in line with their own decisions, views and ideological preferences ( 9 ). In order for an error to lead to bias, it has to be a systematic error. Systematic errors in controlled studies generally cause the results of one group to move in a different direction as compared to the other. It has to be understood that scientific research is generally prone to errors. However, random errors (or, in other words, ‘the luck factor’-in which bias is unintended-do not lead to bias ( 10 ).

Another issue, which is different from bias, is chicanery. It is defined as voluntarily changing the interventions, results and data of patients in an unethical manner or copying data from other studies. Comparatively, bias may not be done consciously.

In case unexpected results or outliers are found while the study is analysed, if possible, such data should be re-included into the study since the complete exclusion of data from a study endangers its reliability. In such a case, evaluation needs to be made with and without outliers. It is insignificant if no difference is found. However, if there is a difference, the results with outliers are re-evaluated. If there is no error, then the outlier is included in the study (as the outlier may be a result). It should be noted that re-evaluation of data in anaesthesiology is not possible.

Statistical evaluation methods should be determined at the design stage so as not to encounter unexpected results in clinical research. The data should be evaluated before the end of the study and without entering into details in research that are time-consuming and involve several samples. This is called an interim analysis . The date of interim analysis should be determined at the beginning of the study. The purpose of making interim analysis is to prevent unnecessary cost and effort since it may be necessary to conclude the research after the interim analysis, e.g. studies in which there is no possibility to validate the hypothesis at the end or the occurrence of different side effects of the drug to be used. The accuracy of the hypothesis and number of samples are compared. Statistical significance levels in interim analysis are very important. If the data level is significant, the hypothesis is validated even if the result turns out to be insignificant after the date of the analysis.

Another important point to be considered is the necessity to conclude the participants’ treatment within the period specified in the study protocol. When the result of the study is achieved earlier and unexpected situations develop, the treatment is concluded earlier. Moreover, the participant may quit the study at its own behest, may die or unpredictable situations (e.g. pregnancy) may develop. The participant can also quit the study whenever it wants, even if the study has not ended ( 7 ).

In case the results of a study are contrary to already known or expected results, the expected quality level of the study suggesting the contradiction may be higher than the studies supporting what is known in that subject. This type of bias is called confirmation bias. The presence of well-known mechanisms and logical inference from them may create problems in the evaluation of data. This is called plausibility bias.

Another type of bias is expectation bias. If a result different from the known results has been achieved and it is against the editor’s will, it can be challenged. Bias may be introduced during the publication of studies, such as publishing only positive results, selection of study results in a way to support a view or prevention of their publication. Some editors may only publish research that extols only the positive results or results that they desire.

Bias may be introduced for advertisement or economic reasons. Economic pressure may be applied on the editor, particularly in the cases of studies involving drugs and new medical devices. This is called commercial bias.

In recent years, before beginning a study, it has been recommended to record it on the Web site www.clinicaltrials.gov for the purpose of facilitating systematic interpretation and analysis in scientific research, informing other researchers, preventing bias, provision of writing in a standard format, enhancing contribution of research results to the general literature and enabling early intervention of an institution for support. This Web site is a service of the US National Institutes of Health.

The last stage in the methodology of clinical studies is the selection of intervention to be conducted. Placebo use assumes an important place in interventions. In Latin, placebo means ‘I will be fine’. In medical literature, it refers to substances that are not curative, do not have active ingredients and have various pharmaceutical forms. Although placebos do not have active drug characteristic, they have shown effective analgesic characteristics, particularly in algology applications; further, its use prevents bias in comparative studies. If a placebo has a positive impact on a participant, it is called the placebo effect ; on the contrary, if it has a negative impact, it is called the nocebo effect . Another type of therapy that can be used in clinical research is sham application. Although a researcher does not cure the patient, the researcher may compare those who receive therapy and undergo sham. It has been seen that sham therapies also exhibit a placebo effect. In particular, sham therapies are used in acupuncture applications ( 11 ). While placebo is a substance, sham is a type of clinical application.

Ethically, the patient has to receive appropriate therapy. For this reason, if its use prevents effective treatment, it causes great problem with regard to patient health and legalities.

Before medical research is conducted with human subjects, predictable risks, drawbacks and benefits must be evaluated for individuals or groups participating in the study. Precautions must be taken for reducing the risk to a minimum level. The risks during the study should be followed, evaluated and recorded by the researcher ( 1 ).

After the methodology for a clinical study is determined, dealing with the ‘Ethics Committee’ forms the next stage. The purpose of the ethics committee is to protect the rights, safety and well-being of volunteers taking part in the clinical research, considering the scientific method and concerns of society. The ethics committee examines the studies presented in time, comprehensively and independently, with regard to ethics and science; in line with the Declaration of Helsinki and following national and international standards concerning ‘Good Clinical Practice’. The method to be followed in the formation of the ethics committee should be developed without any kind of prejudice and to examine the applications with regard to ethics and science within the framework of the ethics committee, Regulation on Clinical Trials and Good Clinical Practice ( www.iku.com ). The necessary documents to be presented to the ethics committee are research protocol, volunteer consent form, budget contract, Declaration of Helsinki, curriculum vitae of researchers, similar or explanatory literature samples, supporting institution approval certificate and patient follow-up form.

Only one sister/brother, mother, father, son/daughter and wife/husband can take charge in the same ethics committee. A rector, vice rector, dean, deputy dean, provincial healthcare director and chief physician cannot be members of the ethics committee.

Members of the ethics committee can work as researchers or coordinators in clinical research. However, during research meetings in which members of the ethics committee are researchers or coordinators, they must leave the session and they cannot sign-off on decisions. If the number of members in the ethics committee for a particular research is so high that it is impossible to take a decision, the clinical research is presented to another ethics committee in the same province. If there is no ethics committee in the same province, an ethics committee in the closest settlement is found.

Thereafter, researchers need to inform the participants using an informed consent form. This form should explain the content of clinical study, potential benefits of the study, alternatives and risks (if any). It should be easy, comprehensible, conforming to spelling rules and written in plain language understandable by the participant.

This form assists the participants in taking a decision regarding participation in the study. It should aim to protect the participants. The participant should be included in the study only after it signs the informed consent form; the participant can quit the study whenever required, even when the study has not ended ( 7 ).

Peer-review: Externally peer-reviewed.

Author Contributions: Concept - C.Ö.Ç., A.D.; Design - C.Ö.Ç.; Supervision - A.D.; Resource - C.Ö.Ç., A.D.; Materials - C.Ö.Ç., A.D.; Analysis and/or Interpretation - C.Ö.Ç., A.D.; Literature Search - C.Ö.Ç.; Writing Manuscript - C.Ö.Ç.; Critical Review - A.D.; Other - C.Ö.Ç., A.D.

Conflict of Interest: No conflict of interest was declared by the authors.

Financial Disclosure: The authors declared that this study has received no financial support.

Definitions of terms in a bachelors', master's or PhD thesis - 3 cases

Finding a suitable definition for a term in a bachelor's thesis, master's thesis or dissertation is often tedious, but absolutely necessary. Otherwise, you start from scratch. There are often many definitions for the same term...

What definition do I use? Fortunately, there are proven methods for searching and formulating definitions. This will help you get a grip on the terms. Let's go!

What is a definition?

A definition always leads a term back to a generic term. In an academic paper, such as a Bachelor's thesis, Master's Thesis or dissertation, the definitions MUST come from recognized sources. But sometimes there aren’t any scientific sources for a research subject, which is especially true when exploring a new field. At that point, you have to formulate a definition yourself.

Three cases can be distinguished with regard to the definition of terms:

• Accepted term - Case 1
• New inconsistent concept - case 2
• New, largely unexplored term (YOUR focus) - Case 3.

Let's go through the cases in order.

Case 1: Definition of an accepted term

The term has been known for a long time and is frequently used in scientific sources. The definitions in different sources are relatively consistent. This can be seen from the fact that the same source references appear repeatedly in definitions.

Examples of such terms are attitudes, motivation, incentives, learning disabilities or controlling.

Such terms are hardly ever discussed anymore. They are simply implied by the definition. Nevertheless, there may be new variations of definitions. However, they are usually for a very specific term and therefore not relevant for your text.

A quick way to get started in defining these terms:

• Be sure to use the correct spelling of the term. Distinguish singular and plural. Search the term in Google.
• Go to Wikipedia and look up the references inside the term article. Focus on scientific sources like books or papers. (Of course you can also do this without a wiki!)
• Locate these sources and gather them. Search at the beginning of the chapter or book for possible definitions. Usually several authors are cited. This is followed by a proposal for a definition, as it is subsequently used in the textbook.
• Adopt this definition, but refer to the original source if it came from another source.
• Write the definition into your text, with the full reference.

IMPORTANT: Do not use Google, Wikipedia, other pure online sources or encyclopedias as a source reference for definitions of recognized terms. It signals carelessness, if not laziness... The only possible sources for the definition of terms are

• textbooks or reference books
• scientific articles (paper)
• lists of standards like DIN, ISO, Law Codices...

By the way, the best sources are standards like DIN and ISO or laws of all kinds. These legal definitions are the best.

Case 2: Definition of still inconsistent term

A characteristic of this type of term is the existence of several definitions by different authors. Ultimately, each definition focuses on specific characteristics. That is why it is often not "either-or", but "both-also".

This is reminiscent of the example with the elephant, which six blind people examine by touch and then describe. The person who touches the trunk says it is a snake. The one sitting on his back says, "That's a mountain." Whoever touches the legs says it is a tree trunk, the ears are ferns, the ivory teeth are field cliffs, etc.

This situation is typical for relatively new subject areas where there is still a lot to discover. New is of course relative and depends on the subject. If there are only five to ten articles on a subject area, this indicates a need for research.

Examples of such terms are social media, trust, mediation.

Proceed as follows when defining these terms for the dissertation:

• Search for the relevant authors on the subject area.
• Search in their scientific articles for the definitions used.
• Make an overview of these definitions. Literally and with reference!!
• Filter out the substance from the respective definitions, the central words and the generic term.
• Check which of these definitions fits your approach.
• Use the appropriate definition or combine several definitions.
• Reconsider and justify your decision. Further work depends on this.
• Ask experts in the field, authors of papers.
• Agree upon the definition with the supervisor of the dissertation.

Case 3: Definition of new, still largely unexplored terms = focus of a dissertation

In this case it is a completely new concept. So far, there are only definitions of experts with experience in the subject area. These have themselves formulated a definition, but it has not been recognized officially. In any case, there are no recognized scientific sources on the field of research to date. But you need a clear definition for your text.

IMPORTANT: Please think very carefully if you really want to work on this topic. The lack of scientifically formulated definitions suggests that this could be an extremely tedious project. You practically have to explore the field without any orientation in the literature. Maybe you are the first to build a model. It could be heroic, but I'm sure it's a lot of work.

This is how you should proceed with new terms in the dissertation:

• Collect all available publications with information on this topic.
• Sort the publications found according to their quality, substance and scientific quality. Use only the best sources (data sources must be traceable and trustworthy)
• Make a comprehensive word cloud of relevant terms and variants.
• Collect the characteristics for the object or terms.
• Think carefully about which other terms are related to the term.
• Filter the ideas and arguments from texts that describe characteristics and are heading towards a definition.
• Make a list of these attributes. These are candidates for the definition.
• Search for generic terms for the term in appropriate documents.
• Make a list.

If you have collected enough sources or five days have passed (whichever happens first):

• Formulate YOUR first definition.
• Leave it for a day or two.
• Check, revise, iterate, collect the evidence, share the definition with others.
• Formulate the working definition for your text. It may be refined along the way.
• Discuss the draft of your definition with the supervisor or even with experts as soon as you are sure you have something to show.

IMPORTANT: Include the reference for each quote.

Now formulate the preliminary working definition that you will use during your research for the dissertation. Refine it if necessary.

Good luck writing your text! Silvio and the Aristolo Team

PS: Check out the Thesis-ABC and the Thesis Guide for writing a bachelor's or master's thesis in 31 days.

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Meaning of thesis in English

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• I wrote my thesis on literacy strategies for boys .
• Her main thesis is that children need a lot of verbal stimulation .
• boilerplate
• composition
• dissertation
• essay question
• peer review

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thesis | Intermediate English

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singing each musical note perfectly, at exactly the right pitch (= level)

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a proposition stated or put forward for consideration, especially one to be discussed and proved or to be maintained against objections: He vigorously defended his thesis on the causes of war.

a subject for a composition or essay.

a dissertation on a particular subject in which one has done original research, as one presented by a candidate for a diploma or degree.

Music . the downward stroke in conducting; downbeat. : Compare arsis (def. 1) .

a part of a metrical foot that does not bear the ictus or stress.

(less commonly) the part of a metrical foot that bears the ictus. : Compare arsis (def. 2) .

Philosophy . See under Hegelian dialectic .

Origin of thesis

Word story for thesis, other words for thesis, words that may be confused with thesis.

• 1. antithesis , synthesis , thesis
• 2. dissertation , thesis

Words Nearby thesis

• shit will hit the fan, the
• shoe is on the other foot, the
• short end of the stick, the
• The show must go on
• thesis play
• thesis statement
• Sketch Book, The
• Skin of Our Teeth, The
• sky's the limit, the

Dictionary.com Unabridged Based on the Random House Unabridged Dictionary, © Random House, Inc. 2024

How to use thesis in a sentence

“The Saudis have been proving the thesis of the film — they do in fact have an army,” said Thor Halvorssen, founder and chief executive of the nonprofit Human Rights Foundation, which funded the movie.

It’s a hypothesis that Bush pursued in her master’s thesis , and last year she began attending virtual Goth parties in a final round of field work before defending her doctoral thesis later this year.

While this partnership was planned prior to the coronavirus outbreak, co-founder Jordana Kier said the pandemic instantly proved out the expansion thesis .

They’ve had to defend that thesis for a very, very long time in front of a variety of different customers and different people.

Over the past decade, In-Q-Tel has been one of the most active investors in the commercial space sector, with a broad investment thesis that touches many aspects of the sector.

In “Back Home,” Gil also revisits the nostalgia for the South explored in his Johns Hopkins thesis , “Circle of Stone.”

At least father and son were in alignment on this central thesis : acting “gay”—bad; being thought of as gay—bad.

Her doctoral thesis , says Ramin Takloo at the University of Illinois, was simply outstanding.

Marshall McLuhan long ago argued the now accepted thesis that different mediums have different influences on thinking.

He wrote his Master's thesis  on the underrepresentation of young people in Congress.

And indeed for most young men a college thesis is but an exercise for sharpening the wits, rarely dangerous in its later effects.

It will be for the reader to determine whether the main thesis of the book has gained or lost by the new evidence.

But the word thesis , when applied to Systems, does not mean the 'position' of single notes, but of groups of notes.

This conclusion, it need hardly be said, is in entire agreement with the main thesis of the preceding pages.

Sundry outlying Indians, with ammunition to waste, took belly and knee rests and strengthened the thesis to the contrary.

British Dictionary definitions for thesis

/ ( ˈθiːsɪs ) /

a dissertation resulting from original research, esp when submitted by a candidate for a degree or diploma

a doctrine maintained or promoted in argument

a subject for a discussion or essay

an unproved statement, esp one put forward as a premise in an argument

music the downbeat of a bar, as indicated in conducting

(in classical prosody) the syllable or part of a metrical foot not receiving the ictus : Compare arsis

philosophy the first stage in the Hegelian dialectic, that is challenged by the antithesis

Collins English Dictionary - Complete & Unabridged 2012 Digital Edition © William Collins Sons & Co. Ltd. 1979, 1986 © HarperCollins Publishers 1998, 2000, 2003, 2005, 2006, 2007, 2009, 2012

Cultural definitions for thesis

The central idea in a piece of writing, sometimes contained in a topic sentence .

The New Dictionary of Cultural Literacy, Third Edition Copyright © 2005 by Houghton Mifflin Harcourt Publishing Company. Published by Houghton Mifflin Harcourt Publishing Company. All rights reserved.

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Scientific Method

Science is an enormously successful human enterprise. The study of scientific method is the attempt to discern the activities by which that success is achieved. Among the activities often identified as characteristic of science are systematic observation and experimentation, inductive and deductive reasoning, and the formation and testing of hypotheses and theories. How these are carried out in detail can vary greatly, but characteristics like these have been looked to as a way of demarcating scientific activity from non-science, where only enterprises which employ some canonical form of scientific method or methods should be considered science (see also the entry on science and pseudo-science ). Others have questioned whether there is anything like a fixed toolkit of methods which is common across science and only science. Some reject privileging one view of method as part of rejecting broader views about the nature of science, such as naturalism (Dupré 2004); some reject any restriction in principle (pluralism).

Scientific method should be distinguished from the aims and products of science, such as knowledge, predictions, or control. Methods are the means by which those goals are achieved. Scientific method should also be distinguished from meta-methodology, which includes the values and justifications behind a particular characterization of scientific method (i.e., a methodology) — values such as objectivity, reproducibility, simplicity, or past successes. Methodological rules are proposed to govern method and it is a meta-methodological question whether methods obeying those rules satisfy given values. Finally, method is distinct, to some degree, from the detailed and contextual practices through which methods are implemented. The latter might range over: specific laboratory techniques; mathematical formalisms or other specialized languages used in descriptions and reasoning; technological or other material means; ways of communicating and sharing results, whether with other scientists or with the public at large; or the conventions, habits, enforced customs, and institutional controls over how and what science is carried out.

While it is important to recognize these distinctions, their boundaries are fuzzy. Hence, accounts of method cannot be entirely divorced from their methodological and meta-methodological motivations or justifications, Moreover, each aspect plays a crucial role in identifying methods. Disputes about method have therefore played out at the detail, rule, and meta-rule levels. Changes in beliefs about the certainty or fallibility of scientific knowledge, for instance (which is a meta-methodological consideration of what we can hope for methods to deliver), have meant different emphases on deductive and inductive reasoning, or on the relative importance attached to reasoning over observation (i.e., differences over particular methods.) Beliefs about the role of science in society will affect the place one gives to values in scientific method.

The issue which has shaped debates over scientific method the most in the last half century is the question of how pluralist do we need to be about method? Unificationists continue to hold out for one method essential to science; nihilism is a form of radical pluralism, which considers the effectiveness of any methodological prescription to be so context sensitive as to render it not explanatory on its own. Some middle degree of pluralism regarding the methods embodied in scientific practice seems appropriate. But the details of scientific practice vary with time and place, from institution to institution, across scientists and their subjects of investigation. How significant are the variations for understanding science and its success? How much can method be abstracted from practice? This entry describes some of the attempts to characterize scientific method or methods, as well as arguments for a more context-sensitive approach to methods embedded in actual scientific practices.

1. Overview and organizing themes

2. historical review: aristotle to mill, 3.1 logical constructionism and operationalism, 3.2. h-d as a logic of confirmation, 3.3. popper and falsificationism, 3.4 meta-methodology and the end of method, 4. statistical methods for hypothesis testing, 5.1 creative and exploratory practices.

• 5.2 Computer methods and the ‘new ways’ of doing science

6.1 “The scientific method” in science education and as seen by scientists

6.2 privileged methods and ‘gold standards’, 6.3 scientific method in the court room, 6.4 deviating practices, 7. conclusion, other internet resources, related entries.

This entry could have been given the title Scientific Methods and gone on to fill volumes, or it could have been extremely short, consisting of a brief summary rejection of the idea that there is any such thing as a unique Scientific Method at all. Both unhappy prospects are due to the fact that scientific activity varies so much across disciplines, times, places, and scientists that any account which manages to unify it all will either consist of overwhelming descriptive detail, or trivial generalizations.

The choice of scope for the present entry is more optimistic, taking a cue from the recent movement in philosophy of science toward a greater attention to practice: to what scientists actually do. This “turn to practice” can be seen as the latest form of studies of methods in science, insofar as it represents an attempt at understanding scientific activity, but through accounts that are neither meant to be universal and unified, nor singular and narrowly descriptive. To some extent, different scientists at different times and places can be said to be using the same method even though, in practice, the details are different.

Whether the context in which methods are carried out is relevant, or to what extent, will depend largely on what one takes the aims of science to be and what one’s own aims are. For most of the history of scientific methodology the assumption has been that the most important output of science is knowledge and so the aim of methodology should be to discover those methods by which scientific knowledge is generated.

Science was seen to embody the most successful form of reasoning (but which form?) to the most certain knowledge claims (but how certain?) on the basis of systematically collected evidence (but what counts as evidence, and should the evidence of the senses take precedence, or rational insight?) Section 2 surveys some of the history, pointing to two major themes. One theme is seeking the right balance between observation and reasoning (and the attendant forms of reasoning which employ them); the other is how certain scientific knowledge is or can be.

Section 3 turns to 20 th century debates on scientific method. In the second half of the 20 th century the epistemic privilege of science faced several challenges and many philosophers of science abandoned the reconstruction of the logic of scientific method. Views changed significantly regarding which functions of science ought to be captured and why. For some, the success of science was better identified with social or cultural features. Historical and sociological turns in the philosophy of science were made, with a demand that greater attention be paid to the non-epistemic aspects of science, such as sociological, institutional, material, and political factors. Even outside of those movements there was an increased specialization in the philosophy of science, with more and more focus on specific fields within science. The combined upshot was very few philosophers arguing any longer for a grand unified methodology of science. Sections 3 and 4 surveys the main positions on scientific method in 20 th century philosophy of science, focusing on where they differ in their preference for confirmation or falsification or for waiving the idea of a special scientific method altogether.

In recent decades, attention has primarily been paid to scientific activities traditionally falling under the rubric of method, such as experimental design and general laboratory practice, the use of statistics, the construction and use of models and diagrams, interdisciplinary collaboration, and science communication. Sections 4–6 attempt to construct a map of the current domains of the study of methods in science.

As these sections illustrate, the question of method is still central to the discourse about science. Scientific method remains a topic for education, for science policy, and for scientists. It arises in the public domain where the demarcation or status of science is at issue. Some philosophers have recently returned, therefore, to the question of what it is that makes science a unique cultural product. This entry will close with some of these recent attempts at discerning and encapsulating the activities by which scientific knowledge is achieved.

Attempting a history of scientific method compounds the vast scope of the topic. This section briefly surveys the background to modern methodological debates. What can be called the classical view goes back to antiquity, and represents a point of departure for later divergences. [ 1 ]

We begin with a point made by Laudan (1968) in his historical survey of scientific method:

Perhaps the most serious inhibition to the emergence of the history of theories of scientific method as a respectable area of study has been the tendency to conflate it with the general history of epistemology, thereby assuming that the narrative categories and classificatory pigeon-holes applied to the latter are also basic to the former. (1968: 5)

To see knowledge about the natural world as falling under knowledge more generally is an understandable conflation. Histories of theories of method would naturally employ the same narrative categories and classificatory pigeon holes. An important theme of the history of epistemology, for example, is the unification of knowledge, a theme reflected in the question of the unification of method in science. Those who have identified differences in kinds of knowledge have often likewise identified different methods for achieving that kind of knowledge (see the entry on the unity of science ).

Different views on what is known, how it is known, and what can be known are connected. Plato distinguished the realms of things into the visible and the intelligible ( The Republic , 510a, in Cooper 1997). Only the latter, the Forms, could be objects of knowledge. The intelligible truths could be known with the certainty of geometry and deductive reasoning. What could be observed of the material world, however, was by definition imperfect and deceptive, not ideal. The Platonic way of knowledge therefore emphasized reasoning as a method, downplaying the importance of observation. Aristotle disagreed, locating the Forms in the natural world as the fundamental principles to be discovered through the inquiry into nature ( Metaphysics Z , in Barnes 1984).

Aristotle is recognized as giving the earliest systematic treatise on the nature of scientific inquiry in the western tradition, one which embraced observation and reasoning about the natural world. In the Prior and Posterior Analytics , Aristotle reflects first on the aims and then the methods of inquiry into nature. A number of features can be found which are still considered by most to be essential to science. For Aristotle, empiricism, careful observation (but passive observation, not controlled experiment), is the starting point. The aim is not merely recording of facts, though. For Aristotle, science ( epistêmê ) is a body of properly arranged knowledge or learning—the empirical facts, but also their ordering and display are of crucial importance. The aims of discovery, ordering, and display of facts partly determine the methods required of successful scientific inquiry. Also determinant is the nature of the knowledge being sought, and the explanatory causes proper to that kind of knowledge (see the discussion of the four causes in the entry on Aristotle on causality ).

In addition to careful observation, then, scientific method requires a logic as a system of reasoning for properly arranging, but also inferring beyond, what is known by observation. Methods of reasoning may include induction, prediction, or analogy, among others. Aristotle’s system (along with his catalogue of fallacious reasoning) was collected under the title the Organon . This title would be echoed in later works on scientific reasoning, such as Novum Organon by Francis Bacon, and Novum Organon Restorum by William Whewell (see below). In Aristotle’s Organon reasoning is divided primarily into two forms, a rough division which persists into modern times. The division, known most commonly today as deductive versus inductive method, appears in other eras and methodologies as analysis/​synthesis, non-ampliative/​ampliative, or even confirmation/​verification. The basic idea is there are two “directions” to proceed in our methods of inquiry: one away from what is observed, to the more fundamental, general, and encompassing principles; the other, from the fundamental and general to instances or implications of principles.

The basic aim and method of inquiry identified here can be seen as a theme running throughout the next two millennia of reflection on the correct way to seek after knowledge: carefully observe nature and then seek rules or principles which explain or predict its operation. The Aristotelian corpus provided the framework for a commentary tradition on scientific method independent of science itself (cosmos versus physics.) During the medieval period, figures such as Albertus Magnus (1206–1280), Thomas Aquinas (1225–1274), Robert Grosseteste (1175–1253), Roger Bacon (1214/1220–1292), William of Ockham (1287–1347), Andreas Vesalius (1514–1546), Giacomo Zabarella (1533–1589) all worked to clarify the kind of knowledge obtainable by observation and induction, the source of justification of induction, and best rules for its application. [ 2 ] Many of their contributions we now think of as essential to science (see also Laudan 1968). As Aristotle and Plato had employed a framework of reasoning either “to the forms” or “away from the forms”, medieval thinkers employed directions away from the phenomena or back to the phenomena. In analysis, a phenomena was examined to discover its basic explanatory principles; in synthesis, explanations of a phenomena were constructed from first principles.

During the Scientific Revolution these various strands of argument, experiment, and reason were forged into a dominant epistemic authority. The 16 th –18 th centuries were a period of not only dramatic advance in knowledge about the operation of the natural world—advances in mechanical, medical, biological, political, economic explanations—but also of self-awareness of the revolutionary changes taking place, and intense reflection on the source and legitimation of the method by which the advances were made. The struggle to establish the new authority included methodological moves. The Book of Nature, according to the metaphor of Galileo Galilei (1564–1642) or Francis Bacon (1561–1626), was written in the language of mathematics, of geometry and number. This motivated an emphasis on mathematical description and mechanical explanation as important aspects of scientific method. Through figures such as Henry More and Ralph Cudworth, a neo-Platonic emphasis on the importance of metaphysical reflection on nature behind appearances, particularly regarding the spiritual as a complement to the purely mechanical, remained an important methodological thread of the Scientific Revolution (see the entries on Cambridge platonists ; Boyle ; Henry More ; Galileo ).

In Novum Organum (1620), Bacon was critical of the Aristotelian method for leaping from particulars to universals too quickly. The syllogistic form of reasoning readily mixed those two types of propositions. Bacon aimed at the invention of new arts, principles, and directions. His method would be grounded in methodical collection of observations, coupled with correction of our senses (and particularly, directions for the avoidance of the Idols, as he called them, kinds of systematic errors to which naïve observers are prone.) The community of scientists could then climb, by a careful, gradual and unbroken ascent, to reliable general claims.

Bacon’s method has been criticized as impractical and too inflexible for the practicing scientist. Whewell would later criticize Bacon in his System of Logic for paying too little attention to the practices of scientists. It is hard to find convincing examples of Bacon’s method being put in to practice in the history of science, but there are a few who have been held up as real examples of 16 th century scientific, inductive method, even if not in the rigid Baconian mold: figures such as Robert Boyle (1627–1691) and William Harvey (1578–1657) (see the entry on Bacon ).

It is to Isaac Newton (1642–1727), however, that historians of science and methodologists have paid greatest attention. Given the enormous success of his Principia Mathematica and Opticks , this is understandable. The study of Newton’s method has had two main thrusts: the implicit method of the experiments and reasoning presented in the Opticks, and the explicit methodological rules given as the Rules for Philosophising (the Regulae) in Book III of the Principia . [ 3 ] Newton’s law of gravitation, the linchpin of his new cosmology, broke with explanatory conventions of natural philosophy, first for apparently proposing action at a distance, but more generally for not providing “true”, physical causes. The argument for his System of the World ( Principia , Book III) was based on phenomena, not reasoned first principles. This was viewed (mainly on the continent) as insufficient for proper natural philosophy. The Regulae counter this objection, re-defining the aims of natural philosophy by re-defining the method natural philosophers should follow. (See the entry on Newton’s philosophy .)

To his list of methodological prescriptions should be added Newton’s famous phrase “ hypotheses non fingo ” (commonly translated as “I frame no hypotheses”.) The scientist was not to invent systems but infer explanations from observations, as Bacon had advocated. This would come to be known as inductivism. In the century after Newton, significant clarifications of the Newtonian method were made. Colin Maclaurin (1698–1746), for instance, reconstructed the essential structure of the method as having complementary analysis and synthesis phases, one proceeding away from the phenomena in generalization, the other from the general propositions to derive explanations of new phenomena. Denis Diderot (1713–1784) and editors of the Encyclopédie did much to consolidate and popularize Newtonianism, as did Francesco Algarotti (1721–1764). The emphasis was often the same, as much on the character of the scientist as on their process, a character which is still commonly assumed. The scientist is humble in the face of nature, not beholden to dogma, obeys only his eyes, and follows the truth wherever it leads. It was certainly Voltaire (1694–1778) and du Chatelet (1706–1749) who were most influential in propagating the latter vision of the scientist and their craft, with Newton as hero. Scientific method became a revolutionary force of the Enlightenment. (See also the entries on Newton , Leibniz , Descartes , Boyle , Hume , enlightenment , as well as Shank 2008 for a historical overview.)

Not all 18 th century reflections on scientific method were so celebratory. Famous also are George Berkeley’s (1685–1753) attack on the mathematics of the new science, as well as the over-emphasis of Newtonians on observation; and David Hume’s (1711–1776) undermining of the warrant offered for scientific claims by inductive justification (see the entries on: George Berkeley ; David Hume ; Hume’s Newtonianism and Anti-Newtonianism ). Hume’s problem of induction motivated Immanuel Kant (1724–1804) to seek new foundations for empirical method, though as an epistemic reconstruction, not as any set of practical guidelines for scientists. Both Hume and Kant influenced the methodological reflections of the next century, such as the debate between Mill and Whewell over the certainty of inductive inferences in science.

The debate between John Stuart Mill (1806–1873) and William Whewell (1794–1866) has become the canonical methodological debate of the 19 th century. Although often characterized as a debate between inductivism and hypothetico-deductivism, the role of the two methods on each side is actually more complex. On the hypothetico-deductive account, scientists work to come up with hypotheses from which true observational consequences can be deduced—hence, hypothetico-deductive. Because Whewell emphasizes both hypotheses and deduction in his account of method, he can be seen as a convenient foil to the inductivism of Mill. However, equally if not more important to Whewell’s portrayal of scientific method is what he calls the “fundamental antithesis”. Knowledge is a product of the objective (what we see in the world around us) and subjective (the contributions of our mind to how we perceive and understand what we experience, which he called the Fundamental Ideas). Both elements are essential according to Whewell, and he was therefore critical of Kant for too much focus on the subjective, and John Locke (1632–1704) and Mill for too much focus on the senses. Whewell’s fundamental ideas can be discipline relative. An idea can be fundamental even if it is necessary for knowledge only within a given scientific discipline (e.g., chemical affinity for chemistry). This distinguishes fundamental ideas from the forms and categories of intuition of Kant. (See the entry on Whewell .)

Clarifying fundamental ideas would therefore be an essential part of scientific method and scientific progress. Whewell called this process “Discoverer’s Induction”. It was induction, following Bacon or Newton, but Whewell sought to revive Bacon’s account by emphasising the role of ideas in the clear and careful formulation of inductive hypotheses. Whewell’s induction is not merely the collecting of objective facts. The subjective plays a role through what Whewell calls the Colligation of Facts, a creative act of the scientist, the invention of a theory. A theory is then confirmed by testing, where more facts are brought under the theory, called the Consilience of Inductions. Whewell felt that this was the method by which the true laws of nature could be discovered: clarification of fundamental concepts, clever invention of explanations, and careful testing. Mill, in his critique of Whewell, and others who have cast Whewell as a fore-runner of the hypothetico-deductivist view, seem to have under-estimated the importance of this discovery phase in Whewell’s understanding of method (Snyder 1997a,b, 1999). Down-playing the discovery phase would come to characterize methodology of the early 20 th century (see section 3 ).

Mill, in his System of Logic , put forward a narrower view of induction as the essence of scientific method. For Mill, induction is the search first for regularities among events. Among those regularities, some will continue to hold for further observations, eventually gaining the status of laws. One can also look for regularities among the laws discovered in a domain, i.e., for a law of laws. Which “law law” will hold is time and discipline dependent and open to revision. One example is the Law of Universal Causation, and Mill put forward specific methods for identifying causes—now commonly known as Mill’s methods. These five methods look for circumstances which are common among the phenomena of interest, those which are absent when the phenomena are, or those for which both vary together. Mill’s methods are still seen as capturing basic intuitions about experimental methods for finding the relevant explanatory factors ( System of Logic (1843), see Mill entry). The methods advocated by Whewell and Mill, in the end, look similar. Both involve inductive generalization to covering laws. They differ dramatically, however, with respect to the necessity of the knowledge arrived at; that is, at the meta-methodological level (see the entries on Whewell and Mill entries).

3. Logic of method and critical responses

The quantum and relativistic revolutions in physics in the early 20 th century had a profound effect on methodology. Conceptual foundations of both theories were taken to show the defeasibility of even the most seemingly secure intuitions about space, time and bodies. Certainty of knowledge about the natural world was therefore recognized as unattainable. Instead a renewed empiricism was sought which rendered science fallible but still rationally justifiable.

Analyses of the reasoning of scientists emerged, according to which the aspects of scientific method which were of primary importance were the means of testing and confirming of theories. A distinction in methodology was made between the contexts of discovery and justification. The distinction could be used as a wedge between the particularities of where and how theories or hypotheses are arrived at, on the one hand, and the underlying reasoning scientists use (whether or not they are aware of it) when assessing theories and judging their adequacy on the basis of the available evidence. By and large, for most of the 20 th century, philosophy of science focused on the second context, although philosophers differed on whether to focus on confirmation or refutation as well as on the many details of how confirmation or refutation could or could not be brought about. By the mid-20 th century these attempts at defining the method of justification and the context distinction itself came under pressure. During the same period, philosophy of science developed rapidly, and from section 4 this entry will therefore shift from a primarily historical treatment of the scientific method towards a primarily thematic one.

Advances in logic and probability held out promise of the possibility of elaborate reconstructions of scientific theories and empirical method, the best example being Rudolf Carnap’s The Logical Structure of the World (1928). Carnap attempted to show that a scientific theory could be reconstructed as a formal axiomatic system—that is, a logic. That system could refer to the world because some of its basic sentences could be interpreted as observations or operations which one could perform to test them. The rest of the theoretical system, including sentences using theoretical or unobservable terms (like electron or force) would then either be meaningful because they could be reduced to observations, or they had purely logical meanings (called analytic, like mathematical identities). This has been referred to as the verifiability criterion of meaning. According to the criterion, any statement not either analytic or verifiable was strictly meaningless. Although the view was endorsed by Carnap in 1928, he would later come to see it as too restrictive (Carnap 1956). Another familiar version of this idea is operationalism of Percy William Bridgman. In The Logic of Modern Physics (1927) Bridgman asserted that every physical concept could be defined in terms of the operations one would perform to verify the application of that concept. Making good on the operationalisation of a concept even as simple as length, however, can easily become enormously complex (for measuring very small lengths, for instance) or impractical (measuring large distances like light years.)

Carl Hempel’s (1950, 1951) criticisms of the verifiability criterion of meaning had enormous influence. He pointed out that universal generalizations, such as most scientific laws, were not strictly meaningful on the criterion. Verifiability and operationalism both seemed too restrictive to capture standard scientific aims and practice. The tenuous connection between these reconstructions and actual scientific practice was criticized in another way. In both approaches, scientific methods are instead recast in methodological roles. Measurements, for example, were looked to as ways of giving meanings to terms. The aim of the philosopher of science was not to understand the methods per se , but to use them to reconstruct theories, their meanings, and their relation to the world. When scientists perform these operations, however, they will not report that they are doing them to give meaning to terms in a formal axiomatic system. This disconnect between methodology and the details of actual scientific practice would seem to violate the empiricism the Logical Positivists and Bridgman were committed to. The view that methodology should correspond to practice (to some extent) has been called historicism, or intuitionism. We turn to these criticisms and responses in section 3.4 . [ 4 ]

Positivism also had to contend with the recognition that a purely inductivist approach, along the lines of Bacon-Newton-Mill, was untenable. There was no pure observation, for starters. All observation was theory laden. Theory is required to make any observation, therefore not all theory can be derived from observation alone. (See the entry on theory and observation in science .) Even granting an observational basis, Hume had already pointed out that one could not deductively justify inductive conclusions without begging the question by presuming the success of the inductive method. Likewise, positivist attempts at analyzing how a generalization can be confirmed by observations of its instances were subject to a number of criticisms. Goodman (1965) and Hempel (1965) both point to paradoxes inherent in standard accounts of confirmation. Recent attempts at explaining how observations can serve to confirm a scientific theory are discussed in section 4 below.

The standard starting point for a non-inductive analysis of the logic of confirmation is known as the Hypothetico-Deductive (H-D) method. In its simplest form, a sentence of a theory which expresses some hypothesis is confirmed by its true consequences. As noted in section 2 , this method had been advanced by Whewell in the 19 th century, as well as Nicod (1924) and others in the 20 th century. Often, Hempel’s (1966) description of the H-D method, illustrated by the case of Semmelweiss’ inferential procedures in establishing the cause of childbed fever, has been presented as a key account of H-D as well as a foil for criticism of the H-D account of confirmation (see, for example, Lipton’s (2004) discussion of inference to the best explanation; also the entry on confirmation ). Hempel described Semmelsweiss’ procedure as examining various hypotheses explaining the cause of childbed fever. Some hypotheses conflicted with observable facts and could be rejected as false immediately. Others needed to be tested experimentally by deducing which observable events should follow if the hypothesis were true (what Hempel called the test implications of the hypothesis), then conducting an experiment and observing whether or not the test implications occurred. If the experiment showed the test implication to be false, the hypothesis could be rejected. If the experiment showed the test implications to be true, however, this did not prove the hypothesis true. The confirmation of a test implication does not verify a hypothesis, though Hempel did allow that “it provides at least some support, some corroboration or confirmation for it” (Hempel 1966: 8). The degree of this support then depends on the quantity, variety and precision of the supporting evidence.

Another approach that took off from the difficulties with inductive inference was Karl Popper’s critical rationalism or falsificationism (Popper 1959, 1963). Falsification is deductive and similar to H-D in that it involves scientists deducing observational consequences from the hypothesis under test. For Popper, however, the important point was not the degree of confirmation that successful prediction offered to a hypothesis. The crucial thing was the logical asymmetry between confirmation, based on inductive inference, and falsification, which can be based on a deductive inference. (This simple opposition was later questioned, by Lakatos, among others. See the entry on historicist theories of scientific rationality. )

Popper stressed that, regardless of the amount of confirming evidence, we can never be certain that a hypothesis is true without committing the fallacy of affirming the consequent. Instead, Popper introduced the notion of corroboration as a measure for how well a theory or hypothesis has survived previous testing—but without implying that this is also a measure for the probability that it is true.

Popper was also motivated by his doubts about the scientific status of theories like the Marxist theory of history or psycho-analysis, and so wanted to demarcate between science and pseudo-science. Popper saw this as an importantly different distinction than demarcating science from metaphysics. The latter demarcation was the primary concern of many logical empiricists. Popper used the idea of falsification to draw a line instead between pseudo and proper science. Science was science because its method involved subjecting theories to rigorous tests which offered a high probability of failing and thus refuting the theory.

A commitment to the risk of failure was important. Avoiding falsification could be done all too easily. If a consequence of a theory is inconsistent with observations, an exception can be added by introducing auxiliary hypotheses designed explicitly to save the theory, so-called ad hoc modifications. This Popper saw done in pseudo-science where ad hoc theories appeared capable of explaining anything in their field of application. In contrast, science is risky. If observations showed the predictions from a theory to be wrong, the theory would be refuted. Hence, scientific hypotheses must be falsifiable. Not only must there exist some possible observation statement which could falsify the hypothesis or theory, were it observed, (Popper called these the hypothesis’ potential falsifiers) it is crucial to the Popperian scientific method that such falsifications be sincerely attempted on a regular basis.

The more potential falsifiers of a hypothesis, the more falsifiable it would be, and the more the hypothesis claimed. Conversely, hypotheses without falsifiers claimed very little or nothing at all. Originally, Popper thought that this meant the introduction of ad hoc hypotheses only to save a theory should not be countenanced as good scientific method. These would undermine the falsifiabililty of a theory. However, Popper later came to recognize that the introduction of modifications (immunizations, he called them) was often an important part of scientific development. Responding to surprising or apparently falsifying observations often generated important new scientific insights. Popper’s own example was the observed motion of Uranus which originally did not agree with Newtonian predictions. The ad hoc hypothesis of an outer planet explained the disagreement and led to further falsifiable predictions. Popper sought to reconcile the view by blurring the distinction between falsifiable and not falsifiable, and speaking instead of degrees of testability (Popper 1985: 41f.).

From the 1960s on, sustained meta-methodological criticism emerged that drove philosophical focus away from scientific method. A brief look at those criticisms follows, with recommendations for further reading at the end of the entry.

Thomas Kuhn’s The Structure of Scientific Revolutions (1962) begins with a well-known shot across the bow for philosophers of science:

History, if viewed as a repository for more than anecdote or chronology, could produce a decisive transformation in the image of science by which we are now possessed. (1962: 1)

The image Kuhn thought needed transforming was the a-historical, rational reconstruction sought by many of the Logical Positivists, though Carnap and other positivists were actually quite sympathetic to Kuhn’s views. (See the entry on the Vienna Circle .) Kuhn shares with other of his contemporaries, such as Feyerabend and Lakatos, a commitment to a more empirical approach to philosophy of science. Namely, the history of science provides important data, and necessary checks, for philosophy of science, including any theory of scientific method.

The history of science reveals, according to Kuhn, that scientific development occurs in alternating phases. During normal science, the members of the scientific community adhere to the paradigm in place. Their commitment to the paradigm means a commitment to the puzzles to be solved and the acceptable ways of solving them. Confidence in the paradigm remains so long as steady progress is made in solving the shared puzzles. Method in this normal phase operates within a disciplinary matrix (Kuhn’s later concept of a paradigm) which includes standards for problem solving, and defines the range of problems to which the method should be applied. An important part of a disciplinary matrix is the set of values which provide the norms and aims for scientific method. The main values that Kuhn identifies are prediction, problem solving, simplicity, consistency, and plausibility.

An important by-product of normal science is the accumulation of puzzles which cannot be solved with resources of the current paradigm. Once accumulation of these anomalies has reached some critical mass, it can trigger a communal shift to a new paradigm and a new phase of normal science. Importantly, the values that provide the norms and aims for scientific method may have transformed in the meantime. Method may therefore be relative to discipline, time or place

Feyerabend also identified the aims of science as progress, but argued that any methodological prescription would only stifle that progress (Feyerabend 1988). His arguments are grounded in re-examining accepted “myths” about the history of science. Heroes of science, like Galileo, are shown to be just as reliant on rhetoric and persuasion as they are on reason and demonstration. Others, like Aristotle, are shown to be far more reasonable and far-reaching in their outlooks then they are given credit for. As a consequence, the only rule that could provide what he took to be sufficient freedom was the vacuous “anything goes”. More generally, even the methodological restriction that science is the best way to pursue knowledge, and to increase knowledge, is too restrictive. Feyerabend suggested instead that science might, in fact, be a threat to a free society, because it and its myth had become so dominant (Feyerabend 1978).

An even more fundamental kind of criticism was offered by several sociologists of science from the 1970s onwards who rejected the methodology of providing philosophical accounts for the rational development of science and sociological accounts of the irrational mistakes. Instead, they adhered to a symmetry thesis on which any causal explanation of how scientific knowledge is established needs to be symmetrical in explaining truth and falsity, rationality and irrationality, success and mistakes, by the same causal factors (see, e.g., Barnes and Bloor 1982, Bloor 1991). Movements in the Sociology of Science, like the Strong Programme, or in the social dimensions and causes of knowledge more generally led to extended and close examination of detailed case studies in contemporary science and its history. (See the entries on the social dimensions of scientific knowledge and social epistemology .) Well-known examinations by Latour and Woolgar (1979/1986), Knorr-Cetina (1981), Pickering (1984), Shapin and Schaffer (1985) seem to bear out that it was social ideologies (on a macro-scale) or individual interactions and circumstances (on a micro-scale) which were the primary causal factors in determining which beliefs gained the status of scientific knowledge. As they saw it therefore, explanatory appeals to scientific method were not empirically grounded.

A late, and largely unexpected, criticism of scientific method came from within science itself. Beginning in the early 2000s, a number of scientists attempting to replicate the results of published experiments could not do so. There may be close conceptual connection between reproducibility and method. For example, if reproducibility means that the same scientific methods ought to produce the same result, and all scientific results ought to be reproducible, then whatever it takes to reproduce a scientific result ought to be called scientific method. Space limits us to the observation that, insofar as reproducibility is a desired outcome of proper scientific method, it is not strictly a part of scientific method. (See the entry on reproducibility of scientific results .)

By the close of the 20 th century the search for the scientific method was flagging. Nola and Sankey (2000b) could introduce their volume on method by remarking that “For some, the whole idea of a theory of scientific method is yester-year’s debate …”.

Despite the many difficulties that philosophers encountered in trying to providing a clear methodology of conformation (or refutation), still important progress has been made on understanding how observation can provide evidence for a given theory. Work in statistics has been crucial for understanding how theories can be tested empirically, and in recent decades a huge literature has developed that attempts to recast confirmation in Bayesian terms. Here these developments can be covered only briefly, and we refer to the entry on confirmation for further details and references.

Statistics has come to play an increasingly important role in the methodology of the experimental sciences from the 19 th century onwards. At that time, statistics and probability theory took on a methodological role as an analysis of inductive inference, and attempts to ground the rationality of induction in the axioms of probability theory have continued throughout the 20 th century and in to the present. Developments in the theory of statistics itself, meanwhile, have had a direct and immense influence on the experimental method, including methods for measuring the uncertainty of observations such as the Method of Least Squares developed by Legendre and Gauss in the early 19 th century, criteria for the rejection of outliers proposed by Peirce by the mid-19 th century, and the significance tests developed by Gosset (a.k.a. “Student”), Fisher, Neyman & Pearson and others in the 1920s and 1930s (see, e.g., Swijtink 1987 for a brief historical overview; and also the entry on C.S. Peirce ).

These developments within statistics then in turn led to a reflective discussion among both statisticians and philosophers of science on how to perceive the process of hypothesis testing: whether it was a rigorous statistical inference that could provide a numerical expression of the degree of confidence in the tested hypothesis, or if it should be seen as a decision between different courses of actions that also involved a value component. This led to a major controversy among Fisher on the one side and Neyman and Pearson on the other (see especially Fisher 1955, Neyman 1956 and Pearson 1955, and for analyses of the controversy, e.g., Howie 2002, Marks 2000, Lenhard 2006). On Fisher’s view, hypothesis testing was a methodology for when to accept or reject a statistical hypothesis, namely that a hypothesis should be rejected by evidence if this evidence would be unlikely relative to other possible outcomes, given the hypothesis were true. In contrast, on Neyman and Pearson’s view, the consequence of error also had to play a role when deciding between hypotheses. Introducing the distinction between the error of rejecting a true hypothesis (type I error) and accepting a false hypothesis (type II error), they argued that it depends on the consequences of the error to decide whether it is more important to avoid rejecting a true hypothesis or accepting a false one. Hence, Fisher aimed for a theory of inductive inference that enabled a numerical expression of confidence in a hypothesis. To him, the important point was the search for truth, not utility. In contrast, the Neyman-Pearson approach provided a strategy of inductive behaviour for deciding between different courses of action. Here, the important point was not whether a hypothesis was true, but whether one should act as if it was.

Similar discussions are found in the philosophical literature. On the one side, Churchman (1948) and Rudner (1953) argued that because scientific hypotheses can never be completely verified, a complete analysis of the methods of scientific inference includes ethical judgments in which the scientists must decide whether the evidence is sufficiently strong or that the probability is sufficiently high to warrant the acceptance of the hypothesis, which again will depend on the importance of making a mistake in accepting or rejecting the hypothesis. Others, such as Jeffrey (1956) and Levi (1960) disagreed and instead defended a value-neutral view of science on which scientists should bracket their attitudes, preferences, temperament, and values when assessing the correctness of their inferences. For more details on this value-free ideal in the philosophy of science and its historical development, see Douglas (2009) and Howard (2003). For a broad set of case studies examining the role of values in science, see e.g. Elliott & Richards 2017.

In recent decades, philosophical discussions of the evaluation of probabilistic hypotheses by statistical inference have largely focused on Bayesianism that understands probability as a measure of a person’s degree of belief in an event, given the available information, and frequentism that instead understands probability as a long-run frequency of a repeatable event. Hence, for Bayesians probabilities refer to a state of knowledge, whereas for frequentists probabilities refer to frequencies of events (see, e.g., Sober 2008, chapter 1 for a detailed introduction to Bayesianism and frequentism as well as to likelihoodism). Bayesianism aims at providing a quantifiable, algorithmic representation of belief revision, where belief revision is a function of prior beliefs (i.e., background knowledge) and incoming evidence. Bayesianism employs a rule based on Bayes’ theorem, a theorem of the probability calculus which relates conditional probabilities. The probability that a particular hypothesis is true is interpreted as a degree of belief, or credence, of the scientist. There will also be a probability and a degree of belief that a hypothesis will be true conditional on a piece of evidence (an observation, say) being true. Bayesianism proscribes that it is rational for the scientist to update their belief in the hypothesis to that conditional probability should it turn out that the evidence is, in fact, observed (see, e.g., Sprenger & Hartmann 2019 for a comprehensive treatment of Bayesian philosophy of science). Originating in the work of Neyman and Person, frequentism aims at providing the tools for reducing long-run error rates, such as the error-statistical approach developed by Mayo (1996) that focuses on how experimenters can avoid both type I and type II errors by building up a repertoire of procedures that detect errors if and only if they are present. Both Bayesianism and frequentism have developed over time, they are interpreted in different ways by its various proponents, and their relations to previous criticism to attempts at defining scientific method are seen differently by proponents and critics. The literature, surveys, reviews and criticism in this area are vast and the reader is referred to the entries on Bayesian epistemology and confirmation .

5. Method in Practice

Attention to scientific practice, as we have seen, is not itself new. However, the turn to practice in the philosophy of science of late can be seen as a correction to the pessimism with respect to method in philosophy of science in later parts of the 20 th century, and as an attempted reconciliation between sociological and rationalist explanations of scientific knowledge. Much of this work sees method as detailed and context specific problem-solving procedures, and methodological analyses to be at the same time descriptive, critical and advisory (see Nickles 1987 for an exposition of this view). The following section contains a survey of some of the practice focuses. In this section we turn fully to topics rather than chronology.

A problem with the distinction between the contexts of discovery and justification that figured so prominently in philosophy of science in the first half of the 20 th century (see section 2 ) is that no such distinction can be clearly seen in scientific activity (see Arabatzis 2006). Thus, in recent decades, it has been recognized that study of conceptual innovation and change should not be confined to psychology and sociology of science, but are also important aspects of scientific practice which philosophy of science should address (see also the entry on scientific discovery ). Looking for the practices that drive conceptual innovation has led philosophers to examine both the reasoning practices of scientists and the wide realm of experimental practices that are not directed narrowly at testing hypotheses, that is, exploratory experimentation.

Examining the reasoning practices of historical and contemporary scientists, Nersessian (2008) has argued that new scientific concepts are constructed as solutions to specific problems by systematic reasoning, and that of analogy, visual representation and thought-experimentation are among the important reasoning practices employed. These ubiquitous forms of reasoning are reliable—but also fallible—methods of conceptual development and change. On her account, model-based reasoning consists of cycles of construction, simulation, evaluation and adaption of models that serve as interim interpretations of the target problem to be solved. Often, this process will lead to modifications or extensions, and a new cycle of simulation and evaluation. However, Nersessian also emphasizes that

creative model-based reasoning cannot be applied as a simple recipe, is not always productive of solutions, and even its most exemplary usages can lead to incorrect solutions. (Nersessian 2008: 11)

Thus, while on the one hand she agrees with many previous philosophers that there is no logic of discovery, discoveries can derive from reasoned processes, such that a large and integral part of scientific practice is

the creation of concepts through which to comprehend, structure, and communicate about physical phenomena …. (Nersessian 1987: 11)

Similarly, work on heuristics for discovery and theory construction by scholars such as Darden (1991) and Bechtel & Richardson (1993) present science as problem solving and investigate scientific problem solving as a special case of problem-solving in general. Drawing largely on cases from the biological sciences, much of their focus has been on reasoning strategies for the generation, evaluation, and revision of mechanistic explanations of complex systems.

Addressing another aspect of the context distinction, namely the traditional view that the primary role of experiments is to test theoretical hypotheses according to the H-D model, other philosophers of science have argued for additional roles that experiments can play. The notion of exploratory experimentation was introduced to describe experiments driven by the desire to obtain empirical regularities and to develop concepts and classifications in which these regularities can be described (Steinle 1997, 2002; Burian 1997; Waters 2007)). However the difference between theory driven experimentation and exploratory experimentation should not be seen as a sharp distinction. Theory driven experiments are not always directed at testing hypothesis, but may also be directed at various kinds of fact-gathering, such as determining numerical parameters. Vice versa , exploratory experiments are usually informed by theory in various ways and are therefore not theory-free. Instead, in exploratory experiments phenomena are investigated without first limiting the possible outcomes of the experiment on the basis of extant theory about the phenomena.

The development of high throughput instrumentation in molecular biology and neighbouring fields has given rise to a special type of exploratory experimentation that collects and analyses very large amounts of data, and these new ‘omics’ disciplines are often said to represent a break with the ideal of hypothesis-driven science (Burian 2007; Elliott 2007; Waters 2007; O’Malley 2007) and instead described as data-driven research (Leonelli 2012; Strasser 2012) or as a special kind of “convenience experimentation” in which many experiments are done simply because they are extraordinarily convenient to perform (Krohs 2012).

5.2 Computer methods and ‘new ways’ of doing science

The field of omics just described is possible because of the ability of computers to process, in a reasonable amount of time, the huge quantities of data required. Computers allow for more elaborate experimentation (higher speed, better filtering, more variables, sophisticated coordination and control), but also, through modelling and simulations, might constitute a form of experimentation themselves. Here, too, we can pose a version of the general question of method versus practice: does the practice of using computers fundamentally change scientific method, or merely provide a more efficient means of implementing standard methods?

Because computers can be used to automate measurements, quantifications, calculations, and statistical analyses where, for practical reasons, these operations cannot be otherwise carried out, many of the steps involved in reaching a conclusion on the basis of an experiment are now made inside a “black box”, without the direct involvement or awareness of a human. This has epistemological implications, regarding what we can know, and how we can know it. To have confidence in the results, computer methods are therefore subjected to tests of verification and validation.

The distinction between verification and validation is easiest to characterize in the case of computer simulations. In a typical computer simulation scenario computers are used to numerically integrate differential equations for which no analytic solution is available. The equations are part of the model the scientist uses to represent a phenomenon or system under investigation. Verifying a computer simulation means checking that the equations of the model are being correctly approximated. Validating a simulation means checking that the equations of the model are adequate for the inferences one wants to make on the basis of that model.

A number of issues related to computer simulations have been raised. The identification of validity and verification as the testing methods has been criticized. Oreskes et al. (1994) raise concerns that “validiation”, because it suggests deductive inference, might lead to over-confidence in the results of simulations. The distinction itself is probably too clean, since actual practice in the testing of simulations mixes and moves back and forth between the two (Weissart 1997; Parker 2008a; Winsberg 2010). Computer simulations do seem to have a non-inductive character, given that the principles by which they operate are built in by the programmers, and any results of the simulation follow from those in-built principles in such a way that those results could, in principle, be deduced from the program code and its inputs. The status of simulations as experiments has therefore been examined (Kaufmann and Smarr 1993; Humphreys 1995; Hughes 1999; Norton and Suppe 2001). This literature considers the epistemology of these experiments: what we can learn by simulation, and also the kinds of justifications which can be given in applying that knowledge to the “real” world. (Mayo 1996; Parker 2008b). As pointed out, part of the advantage of computer simulation derives from the fact that huge numbers of calculations can be carried out without requiring direct observation by the experimenter/​simulator. At the same time, many of these calculations are approximations to the calculations which would be performed first-hand in an ideal situation. Both factors introduce uncertainties into the inferences drawn from what is observed in the simulation.

For many of the reasons described above, computer simulations do not seem to belong clearly to either the experimental or theoretical domain. Rather, they seem to crucially involve aspects of both. This has led some authors, such as Fox Keller (2003: 200) to argue that we ought to consider computer simulation a “qualitatively different way of doing science”. The literature in general tends to follow Kaufmann and Smarr (1993) in referring to computer simulation as a “third way” for scientific methodology (theoretical reasoning and experimental practice are the first two ways.). It should also be noted that the debates around these issues have tended to focus on the form of computer simulation typical in the physical sciences, where models are based on dynamical equations. Other forms of simulation might not have the same problems, or have problems of their own (see the entry on computer simulations in science ).

In recent years, the rapid development of machine learning techniques has prompted some scholars to suggest that the scientific method has become “obsolete” (Anderson 2008, Carrol and Goodstein 2009). This has resulted in an intense debate on the relative merit of data-driven and hypothesis-driven research (for samples, see e.g. Mazzocchi 2015 or Succi and Coveney 2018). For a detailed treatment of this topic, we refer to the entry scientific research and big data .

6. Discourse on scientific method

Despite philosophical disagreements, the idea of the scientific method still figures prominently in contemporary discourse on many different topics, both within science and in society at large. Often, reference to scientific method is used in ways that convey either the legend of a single, universal method characteristic of all science, or grants to a particular method or set of methods privilege as a special ‘gold standard’, often with reference to particular philosophers to vindicate the claims. Discourse on scientific method also typically arises when there is a need to distinguish between science and other activities, or for justifying the special status conveyed to science. In these areas, the philosophical attempts at identifying a set of methods characteristic for scientific endeavors are closely related to the philosophy of science’s classical problem of demarcation (see the entry on science and pseudo-science ) and to the philosophical analysis of the social dimension of scientific knowledge and the role of science in democratic society.

One of the settings in which the legend of a single, universal scientific method has been particularly strong is science education (see, e.g., Bauer 1992; McComas 1996; Wivagg & Allchin 2002). [ 5 ] Often, ‘the scientific method’ is presented in textbooks and educational web pages as a fixed four or five step procedure starting from observations and description of a phenomenon and progressing over formulation of a hypothesis which explains the phenomenon, designing and conducting experiments to test the hypothesis, analyzing the results, and ending with drawing a conclusion. Such references to a universal scientific method can be found in educational material at all levels of science education (Blachowicz 2009), and numerous studies have shown that the idea of a general and universal scientific method often form part of both students’ and teachers’ conception of science (see, e.g., Aikenhead 1987; Osborne et al. 2003). In response, it has been argued that science education need to focus more on teaching about the nature of science, although views have differed on whether this is best done through student-led investigations, contemporary cases, or historical cases (Allchin, Andersen & Nielsen 2014)

Although occasionally phrased with reference to the H-D method, important historical roots of the legend in science education of a single, universal scientific method are the American philosopher and psychologist Dewey’s account of inquiry in How We Think (1910) and the British mathematician Karl Pearson’s account of science in Grammar of Science (1892). On Dewey’s account, inquiry is divided into the five steps of

(i) a felt difficulty, (ii) its location and definition, (iii) suggestion of a possible solution, (iv) development by reasoning of the bearing of the suggestions, (v) further observation and experiment leading to its acceptance or rejection. (Dewey 1910: 72)

Similarly, on Pearson’s account, scientific investigations start with measurement of data and observation of their correction and sequence from which scientific laws can be discovered with the aid of creative imagination. These laws have to be subject to criticism, and their final acceptance will have equal validity for “all normally constituted minds”. Both Dewey’s and Pearson’s accounts should be seen as generalized abstractions of inquiry and not restricted to the realm of science—although both Dewey and Pearson referred to their respective accounts as ‘the scientific method’.

Occasionally, scientists make sweeping statements about a simple and distinct scientific method, as exemplified by Feynman’s simplified version of a conjectures and refutations method presented, for example, in the last of his 1964 Cornell Messenger lectures. [ 6 ] However, just as often scientists have come to the same conclusion as recent philosophy of science that there is not any unique, easily described scientific method. For example, the physicist and Nobel Laureate Weinberg described in the paper “The Methods of Science … And Those By Which We Live” (1995) how

The fact that the standards of scientific success shift with time does not only make the philosophy of science difficult; it also raises problems for the public understanding of science. We do not have a fixed scientific method to rally around and defend. (1995: 8)

Interview studies with scientists on their conception of method shows that scientists often find it hard to figure out whether available evidence confirms their hypothesis, and that there are no direct translations between general ideas about method and specific strategies to guide how research is conducted (Schickore & Hangel 2019, Hangel & Schickore 2017)

Reference to the scientific method has also often been used to argue for the scientific nature or special status of a particular activity. Philosophical positions that argue for a simple and unique scientific method as a criterion of demarcation, such as Popperian falsification, have often attracted practitioners who felt that they had a need to defend their domain of practice. For example, references to conjectures and refutation as the scientific method are abundant in much of the literature on complementary and alternative medicine (CAM)—alongside the competing position that CAM, as an alternative to conventional biomedicine, needs to develop its own methodology different from that of science.

Also within mainstream science, reference to the scientific method is used in arguments regarding the internal hierarchy of disciplines and domains. A frequently seen argument is that research based on the H-D method is superior to research based on induction from observations because in deductive inferences the conclusion follows necessarily from the premises. (See, e.g., Parascandola 1998 for an analysis of how this argument has been made to downgrade epidemiology compared to the laboratory sciences.) Similarly, based on an examination of the practices of major funding institutions such as the National Institutes of Health (NIH), the National Science Foundation (NSF) and the Biomedical Sciences Research Practices (BBSRC) in the UK, O’Malley et al. (2009) have argued that funding agencies seem to have a tendency to adhere to the view that the primary activity of science is to test hypotheses, while descriptive and exploratory research is seen as merely preparatory activities that are valuable only insofar as they fuel hypothesis-driven research.

In some areas of science, scholarly publications are structured in a way that may convey the impression of a neat and linear process of inquiry from stating a question, devising the methods by which to answer it, collecting the data, to drawing a conclusion from the analysis of data. For example, the codified format of publications in most biomedical journals known as the IMRAD format (Introduction, Method, Results, Analysis, Discussion) is explicitly described by the journal editors as “not an arbitrary publication format but rather a direct reflection of the process of scientific discovery” (see the so-called “Vancouver Recommendations”, ICMJE 2013: 11). However, scientific publications do not in general reflect the process by which the reported scientific results were produced. For example, under the provocative title “Is the scientific paper a fraud?”, Medawar argued that scientific papers generally misrepresent how the results have been produced (Medawar 1963/1996). Similar views have been advanced by philosophers, historians and sociologists of science (Gilbert 1976; Holmes 1987; Knorr-Cetina 1981; Schickore 2008; Suppe 1998) who have argued that scientists’ experimental practices are messy and often do not follow any recognizable pattern. Publications of research results, they argue, are retrospective reconstructions of these activities that often do not preserve the temporal order or the logic of these activities, but are instead often constructed in order to screen off potential criticism (see Schickore 2008 for a review of this work).

Philosophical positions on the scientific method have also made it into the court room, especially in the US where judges have drawn on philosophy of science in deciding when to confer special status to scientific expert testimony. A key case is Daubert vs Merrell Dow Pharmaceuticals (92–102, 509 U.S. 579, 1993). In this case, the Supreme Court argued in its 1993 ruling that trial judges must ensure that expert testimony is reliable, and that in doing this the court must look at the expert’s methodology to determine whether the proffered evidence is actually scientific knowledge. Further, referring to works of Popper and Hempel the court stated that

ordinarily, a key question to be answered in determining whether a theory or technique is scientific knowledge … is whether it can be (and has been) tested. (Justice Blackmun, Daubert v. Merrell Dow Pharmaceuticals; see Other Internet Resources for a link to the opinion)

But as argued by Haack (2005a,b, 2010) and by Foster & Hubner (1999), by equating the question of whether a piece of testimony is reliable with the question whether it is scientific as indicated by a special methodology, the court was producing an inconsistent mixture of Popper’s and Hempel’s philosophies, and this has later led to considerable confusion in subsequent case rulings that drew on the Daubert case (see Haack 2010 for a detailed exposition).

The difficulties around identifying the methods of science are also reflected in the difficulties of identifying scientific misconduct in the form of improper application of the method or methods of science. One of the first and most influential attempts at defining misconduct in science was the US definition from 1989 that defined misconduct as

fabrication, falsification, plagiarism, or other practices that seriously deviate from those that are commonly accepted within the scientific community . (Code of Federal Regulations, part 50, subpart A., August 8, 1989, italics added)

However, the “other practices that seriously deviate” clause was heavily criticized because it could be used to suppress creative or novel science. For example, the National Academy of Science stated in their report Responsible Science (1992) that it

wishes to discourage the possibility that a misconduct complaint could be lodged against scientists based solely on their use of novel or unorthodox research methods. (NAS: 27)

This clause was therefore later removed from the definition. For an entry into the key philosophical literature on conduct in science, see Shamoo & Resnick (2009).

The question of the source of the success of science has been at the core of philosophy since the beginning of modern science. If viewed as a matter of epistemology more generally, scientific method is a part of the entire history of philosophy. Over that time, science and whatever methods its practitioners may employ have changed dramatically. Today, many philosophers have taken up the banners of pluralism or of practice to focus on what are, in effect, fine-grained and contextually limited examinations of scientific method. Others hope to shift perspectives in order to provide a renewed general account of what characterizes the activity we call science.

One such perspective has been offered recently by Hoyningen-Huene (2008, 2013), who argues from the history of philosophy of science that after three lengthy phases of characterizing science by its method, we are now in a phase where the belief in the existence of a positive scientific method has eroded and what has been left to characterize science is only its fallibility. First was a phase from Plato and Aristotle up until the 17 th century where the specificity of scientific knowledge was seen in its absolute certainty established by proof from evident axioms; next was a phase up to the mid-19 th century in which the means to establish the certainty of scientific knowledge had been generalized to include inductive procedures as well. In the third phase, which lasted until the last decades of the 20 th century, it was recognized that empirical knowledge was fallible, but it was still granted a special status due to its distinctive mode of production. But now in the fourth phase, according to Hoyningen-Huene, historical and philosophical studies have shown how “scientific methods with the characteristics as posited in the second and third phase do not exist” (2008: 168) and there is no longer any consensus among philosophers and historians of science about the nature of science. For Hoyningen-Huene, this is too negative a stance, and he therefore urges the question about the nature of science anew. His own answer to this question is that “scientific knowledge differs from other kinds of knowledge, especially everyday knowledge, primarily by being more systematic” (Hoyningen-Huene 2013: 14). Systematicity can have several different dimensions: among them are more systematic descriptions, explanations, predictions, defense of knowledge claims, epistemic connectedness, ideal of completeness, knowledge generation, representation of knowledge and critical discourse. Hence, what characterizes science is the greater care in excluding possible alternative explanations, the more detailed elaboration with respect to data on which predictions are based, the greater care in detecting and eliminating sources of error, the more articulate connections to other pieces of knowledge, etc. On this position, what characterizes science is not that the methods employed are unique to science, but that the methods are more carefully employed.

Another, similar approach has been offered by Haack (2003). She sets off, similar to Hoyningen-Huene, from a dissatisfaction with the recent clash between what she calls Old Deferentialism and New Cynicism. The Old Deferentialist position is that science progressed inductively by accumulating true theories confirmed by empirical evidence or deductively by testing conjectures against basic statements; while the New Cynics position is that science has no epistemic authority and no uniquely rational method and is merely just politics. Haack insists that contrary to the views of the New Cynics, there are objective epistemic standards, and there is something epistemologically special about science, even though the Old Deferentialists pictured this in a wrong way. Instead, she offers a new Critical Commonsensist account on which standards of good, strong, supportive evidence and well-conducted, honest, thorough and imaginative inquiry are not exclusive to the sciences, but the standards by which we judge all inquirers. In this sense, science does not differ in kind from other kinds of inquiry, but it may differ in the degree to which it requires broad and detailed background knowledge and a familiarity with a technical vocabulary that only specialists may possess.

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• Blackmun opinion , in Daubert v. Merrell Dow Pharmaceuticals (92–102), 509 U.S. 579 (1993).
• Scientific Method at philpapers. Darrell Rowbottom (ed.).
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