definition of hypothesis in punjabi

English Punjabi Dictionary | ਅੰਗ੍ਰੇਜ਼ੀ ਪੰਜਾਬੀ ਸ਼ਬਦ-ਕੋਸ਼

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hypothesis - Meaning in Punjabi

Hypothesis word forms & inflections, definitions and meaning of hypothesis in english, hypothesis noun.

• possibility , theory
• "a scientific hypothesis that survives experimental testing becomes a scientific theory"
• "he proposed a fresh theory of alkalis that later was accepted in chemical practices"
• conjecture , guess , speculation , supposition , surmisal , surmise

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Synonyms of hypothesis

A hypothesis is a proposed explanation for a phenomenon. For a hypothesis to be a scientific hypothesis, the scientific method requires that one can test it. Scientists generally base scientific hypotheses on previous observations that cannot satisfactorily be explained with the available scientific theories. Even though the words "hypothesis" and "theory" are often used interchangeably, a scientific hypothesis is not the same as a scientific theory. A working hypothesis is a provisionally accepted hypothesis proposed for further research in a process beginning with an educated guess or thought.

ਮਨੌਤ ਕਿਸੇ ਘਟਨਾ ਦਾ ਵੇਰਵਾ ਦੇਣ ਵਾਸਤੇ ਤਜਵੀਜ਼ ਕੀਤੀ ਗਈ ਭਾਵ ਵਿਚਾਰ ਗੋਚਰੇ ਰੱਖੀ ਗਈ ਇੱਕ ਵਿਆਖਿਆ ਹੁੰਦੀ ਹੈ। ਵਿਗਿਆਨਕ ਤਰੀਕੇ ਮੁਤਾਬਕ ਕੋਈ ਮਨੌਤ ਵਿਗਿਆਨਕ ਮਨੌਤ ਸਿਰਫ਼ ਉਦੋਂ ਬਣਦੀ ਹੈ ਜਦੋਂ ਉਹ ਪਰਖਣਯੋਗ ਹੋਵੇ। ਵਿਗਿਆਨਕ ਮਨੌਤ ਅਤੇ ਵਿਗਿਆਨਕ ਸਿਧਾਂਤ ਵਿੱਚ ਫ਼ਰਕ ਹੁੰਦਾ ਹੈ। ਕਾਰਜਕਾਰੀ ਮਨੌਤ ਆਰਜ਼ੀ ਤੌਰ ਉੱਤੇ ਕਬੂਲੀ ਗਈ ਮਨੌਤ ਹੁੰਦੀ ਹੈ ਜੀਹਨੂੰ ਅੱਗੋਂ ਦੀ ਘੋਖ ਕਰਨ ਵਾਸਤੇ ਪੇਸ਼ ਕੀਤਾ ਜਾਂਦਾ ਹੈ।

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What is hypothesis meaning in Punjabi, hypothesis translation in Punjabi, hypothesis definition, pronunciations and examples of hypothesis in Punjabi.

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English to Punjabi Meaning of hypothesis - ਪਰਿਕਲਪਨਾ

ਪਰਿਕਲਪਨਾ, ਰਵ, ਅਨੁਿਾਨ, ਮੰਨਦੇ

Let's say for a moment that I accept the bath-item-gift HYPOTHESIS .

...on your tremendously exciting and not yet conclusively disproved HYPOTHESIS .

Interesting HYPOTHESIS .

Okay. All right. Let's assume your HYPOTHESIS .

Well, that's certainly a thought-provoking HYPOTHESIS .

Meaning and definitions of hypothesis, translation in Punjabi language for hypothesis with similar and opposite words. Also find spoken pronunciation of hypothesis in Punjabi and in English language.

What hypothesis means in Punjabi, hypothesis meaning in Punjabi, hypothesis definition, examples and pronunciation of hypothesis in Punjabi language.

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hypothesis Punjabi Meaning

Punjabi Meaning for hypothesis

n. khayal. M; anman. M;

Punjabi Shahmukhi Dictionary

English to Punjabi Shahmukhi Dictionary

Definitions

English definition for hypothesis

1. n. a tentative theory about the natural world; a concept that is not yet verified but that if true would explain certain facts or phenomena

2. n. a message expressing an opinion based on incomplete evidence

3. n. a proposal intended to explain certain facts or observations

Synonyms and Antonyms for hypothesis

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International Languages

Meaning for hypothesis found in 56 Languages.

Near By Words

• hypothesist

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Hypothesis Meaning In Punjabi

ਸਧਾਰਨ ਉਦਾਹਰਣਾਂ ਅਤੇ ਪਰਿਭਾਸ਼ਾਵਾਂ ਦੇ ਨਾਲ hypothesis ਦਾ ਅਸਲ ਅਰਥ ਜਾਣੋ।., ਪਰਿਭਾਸ਼ਾਵਾਂ, definitions of hypothesis.

1 . ਅੱਗੇ ਦੀ ਜਾਂਚ ਲਈ ਸ਼ੁਰੂਆਤੀ ਬਿੰਦੂ ਵਜੋਂ ਸੀਮਤ ਸਬੂਤਾਂ ਦੇ ਆਧਾਰ 'ਤੇ ਪੇਸ਼ ਕੀਤੀ ਗਈ ਪਰਿਕਲਪਨਾ ਜਾਂ ਸਪੱਸ਼ਟੀਕਰਨ।

1 . a supposition or proposed explanation made on the basis of limited evidence as a starting point for further investigation.

ਸਮਾਨਾਰਥੀ ਸ਼ਬਦ

Examples of hypothesis :.

1 . ਪੈਨਸਪਰਮੀਆ ਪਰਿਕਲਪਨਾ ਵਿਕਲਪਿਕ ਤੌਰ 'ਤੇ ਸੁਝਾਅ ਦਿੰਦੀ ਹੈ ਕਿ ਮਾਈਕ੍ਰੋਸਕੋਪਿਕ ਜੀਵਨ ਨੂੰ ਸ਼ੁਰੂਆਤੀ ਧਰਤੀ 'ਤੇ meteorites, asteroids ਅਤੇ ਹੋਰ ਛੋਟੇ ਸੂਰਜੀ ਸਿਸਟਮ ਦੇ ਸਰੀਰ ਦੁਆਰਾ ਵੰਡਿਆ ਗਿਆ ਸੀ ਅਤੇ ਇਹ ਜੀਵਨ ਪੂਰੇ ਬ੍ਰਹਿਮੰਡ ਵਿੱਚ ਮੌਜੂਦ ਹੋ ਸਕਦਾ ਹੈ।

1 . the panspermia hypothesis alternatively suggests that microscopic life was distributed to the early earth by meteoroids, asteroids and other small solar system bodies and that life may exist throughout the universe.

2 . ਨਲ ਪਰਿਕਲਪਨਾ ਟੈਸਟਿੰਗ ਮਹੱਤਵਪੂਰਨ ਹੈ.

2 . Null hypothesis testing is important.

3 . ਨਿਰੰਤਰ ਪਰਿਕਲਪਨਾ

3 . the continuum hypothesis .

4 . ਇੱਕ ਅਪ੍ਰਮਾਣਿਤ ਪਰਿਕਲਪਨਾ

4 . an unverifiable hypothesis

5 . ਹਰੇਕ ਪਰਿਕਲਪਨਾ, ਜੋ ਕਿਸੇ ਇੱਕ ਓਨਟੋਲੋਜੀਕਲ ਸਿਧਾਂਤਾਂ ਦਾ ਖੰਡਨ ਕਰਦੀ ਹੈ, ਗਲਤ ਹੈ।

5 . Each hypothesis , which contradicts one of the ontological principles, is wrong.

6 . ਪੈਨਸਪਰਮੀਆ ਪਰਿਕਲਪਨਾ ਇਹ ਦਰਸਾਉਂਦੀ ਹੈ ਕਿ ਸੂਖਮ ਜੀਵਨ ਨੂੰ ਸ਼ੁਰੂਆਤੀ ਧਰਤੀ 'ਤੇ ਪੁਲਾੜ ਦੀ ਧੂੜ, ਮੀਟੋਰਾਈਟਸ, ਐਸਟੋਰਾਇਡਜ਼ ਅਤੇ ਹੋਰ ਛੋਟੇ ਸੂਰਜੀ ਸਿਸਟਮਾਂ ਦੁਆਰਾ ਵੰਡਿਆ ਗਿਆ ਸੀ ਅਤੇ ਇਹ ਜੀਵਨ ਪੂਰੇ ਬ੍ਰਹਿਮੰਡ ਵਿੱਚ ਮੌਜੂਦ ਹੋ ਸਕਦਾ ਹੈ।

6 . the panspermia hypothesis suggests that microscopic life was distributed to the early earth by space dust, meteoroids, asteroids and other small solar system bodies and that life may exist throughout the universe.

7 . ਇੱਕ ਪਰੀਖਣਯੋਗ ਪਰਿਕਲਪਨਾ

7 . a testable hypothesis

8 . ਫੈਂਟਮ ਟਾਈਮ ਕਲਪਨਾ.

8 . phantom time hypothesis .

9 . ਇਸ ਧਾਰਨਾ ਨੂੰ ਪਰਖਣ ਲਈ, ਡਾ.

9 . to test this hypothesis , dr.

10 . ਮੈਂ ਆਪਣੀ ਧਾਰਨਾ ਵਿੱਚ ਸਹੀ ਸੀ।

10 . i was correct in my hypothesis .

11 . ਖੁਸ਼ੀ ਦੀ ਕਲਪਨਾ" ਹੈਡਟ.

11 . the happiness hypothesis " haidt.

12 . ਇਹ ਢਲਾਨ ਇੱਕ ਨਵੀਂ ਪਰਿਕਲਪਨਾ ਨੂੰ ਉਭਾਰਦਾ ਹੈ।

12 . this slope raises a new hypothesis .

13 . ਹਾਈਪੋਥੀਸਿਸ ਬੀ: ਇਤਿਹਾਸ ਦਾ ਇੱਕ ਅਰਥ ਹੁੰਦਾ ਹੈ।

13 . Hypothesis B: history has a meaning.

14 . ਇਹ sapir-whorf ਪਰਿਕਲਪਨਾ ਨਾਲ ਮੇਲ ਖਾਂਦਾ ਹੈ।

14 . this corresponds to the sapir-whorf hypothesis .

15 . ਮੇਰੇ ਕੋਲ ਇੱਕ ਅਨੁਮਾਨ ਹੈ ਜੋ ਸਹੀ ਹੋ ਸਕਦਾ ਹੈ ਜਾਂ ਨਹੀਂ।

15 . i have a hypothesis that may or may not be right.

16 . ਪਰਿਕਲਪਨਾ ਸਿਰਫ਼ ਅਪ੍ਰਵਾਨਯੋਗ ਨਹੀਂ ਹੈ, ਪਰ ਝੂਠੀ ਹੈ

16 . the hypothesis is not merely unprovable, but false

17 . ਉਸਨੇ ਸਾਰਿਆਂ ਨੂੰ ਸਹੁੰ ਖਾਧੀ ਕਿ ਪਰਿਕਲਪਨਾ ਕੰਮ ਕਰੇਗੀ।

17 . She swore to everyone that the hypothesis would work.

18 . … ਜਾਂ ਪਰਿਕਲਪਨਾ ਨੂੰ ਰੱਦ ਕਰਨ ਲਈ ਇੱਕ "ਬੇਨਤੀ"?

18 . … or a “Requiem” for the rejection of the hypothesis ?

19 . ਤੁਸੀਂ ਇੱਕ ਨਲ ਪਰਿਕਲਪਨਾ ਨੂੰ ਰੱਦ ਕਰਨ ਲਈ ANOVA ਦੀ ਵਰਤੋਂ ਕਿਵੇਂ ਕਰ ਸਕਦੇ ਹੋ?

19 . How can you use the ANOVA to reject a null hypothesis ?

20 . ਬਹੁਤ ਅੱਛਾ; ਤੁਸੀਂ ਇਸ ਹੋਰ ਪਰਿਕਲਪਨਾ ਬਾਰੇ ਕੀ ਕਹਿੰਦੇ ਹੋ?

20 . Very good; what say you then to this other hypothesis ?

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Hypothesis meaning in Punjabi - Learn actual meaning of Hypothesis with simple examples & definitions. Also you will learn Antonyms , synonyms & best example sentences. This dictionary also provide you 10 languages so you can find meaning of Hypothesis in Hindi, Tamil , Telugu , Bengali , Kannada , Marathi , Malayalam , Gujarati , Punjabi , Urdu.

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• Vaḍḍī Panjābī lughat : Punjabi dictionary (meanings in Punjabi) by Iqbal Salah-ud-Din (2002)

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• The Panjabi dictionary by Maya Singh (1895)

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• A dictionary of the Panjābī language , Lodiana Mission, Presbyterian Church (1854)

• Idiomatic sentences in English and Panjabi (1846)

• A dictionary, English and Punjabee outlines of grammar, also dialogues , by Samuel Starkey (1849)

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• Morphology of Persian loan nouns in Punjabi by Mehwish Noor, Riaz Ahmed Mangrio, Behzad Anwar, in Pakistan Journal of social issues (2019)

• Les mouvements pour le panjabi à Lahore entre 1947 et 1960 (Pakistan) by Julien-Régis Columeau, thesis (2019)

• Teach yourself Panjabi by Hardev Bahri (2011)

• An introduction to Punjabi : grammar, conversation and literature , by Gurinder Singh Mann, Gurdit Singh, Ami Shah, Gibb Schreffler, Anne Murphy (2011)

• Let us learn Gurmukhi by Shamsher Singh Puri (2004)

• Teach yourself Panjabi (1999)

• Panjabi made easy by J. S. Nagra (1988): I & II - III

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• Panjabi manual and grammar : a guide to the colloquial Panjabi , by Thomas Grahame Bailey (1912)

• A Panjabi phonetic reader (1914)

• A guide to Panjabi by Jawahir Singh (1902)

• Grammar of Panjabee language by R. Leech (1838)

• Specimens of Pañjābī by George Abraham Grierson, in Linguistic survey of India (1919)

manuscript in Punjabi

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• studies about the Punjabi literature and Sikhism, by Denis Matringe

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• Genesis , with the first twenty chapters of Exodus , by Levi Janvier (1849)

ਸਾਰਾ ਮਨੁੱਖੀ ਪਰਿਵਾਰ ਆਪਣੀ ਮਹਿਮਾ, ਸ਼ਾਨ ਅਤੇ ਹੱਕਾਂ ਦੇ ਪੱਖੋਂ ਜਨਮ ਤੋਂ ਹੀ ਆਜ਼ਾਦ ਹੈ ਅਤੇ ਸੁਤੇ ਸਿੱਧ ਸਾਰੇ ਲੋਕ ਬਰਾਬਰ ਹਨ । ਉਨ੍ਹਾਂ ਸਭਨਾ ਨੂੰ ਤਰਕ ਅਤੇ ਜ਼ਮੀਰ ਦੀ ਸੌਗਾਤ ਮਿਲੀ ਹੋਈ ਹੈ ਅਤੇ ਉਨ੍ਹਾਂ ਨੂੰ ਭਰਾਤਰੀਭਾਵ ਦੀ ਭਾਵਨਾ ਰਖਦਿਆਂ ਆਪਸ ਵਿਚ ਵਿਚਰਣਾ ਚਾਹੀਦਾ ਹੈ ।

• Universal Declaration of Human Rights : translation into Punjabi (+ audio)

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hypothesis in Gujarati ગુજરાતી

• ગૃહીત માની લીધેલી–વાત ⇄ hypothesis gujarati
• પૂર્વધારણ ા ⇄ hypothesis gujarati
• પૂર્વસિદ્ધાન્ત ⇄ hypothesis gujarati

hypothesis in Hindi हिन्दी

• परिकल्पना ⇄ hypothesis hindi
• प्रकल्पना ⇄ hypothesis hindi
• प्रमेय ⇄ hypothesis hindi
• प्राक्कल्पना ⇄ hypothesis hindi

hypothesis in Kashmiri कॉशुर

• فَرضی دعوا ⇄ hypothesis kashmiri

hypothesis in Maithili মৈথিলী

• अवधारणा ⇄ Hypothesis maithili

hypothesis in Marathi मराठी

• अभ्युपगम ⇄ hypothesis marathi
• गृहीत ⇄ hypothesis marathi
• गृहीत उपन्यास ⇄ hypothesis marathi
• गृहीत धरलेली गोष्ट ⇄ hypothesis marathi
• गृहीत पक्ष ⇄ hypothesis marathi
• गृहीतक ⇄ hypothesis marathi
• गृहीतकल्पना ⇄ hypothesis marathi
• गृहीतकृत्य ⇄ hypothesis marathi
• पक्ष ⇄ hypothesis marathi
• परिकल्पना ⇄ hypothesis marathi
• प्रमेय ⇄ hypothesis marathi

hypothesis in Santali

• उपकल्पना ⇄ hypothesis sanskrit

hypothesis in Sindhi سنڌي

• مفروضو، قياس، دعويٰ، فرض ⇄ Hypothesis sindhi

hypothesis in Telugu తెలుగు

• అనుమానము ⇄ hypothesis telugu
• ఊహ ⇄ hypothesis telugu
• ఎన్నిక. తెలుగుసంస్కృతములో నుంచి పుట్టినదనే పక్షమును నేను వొప్పలేదు ⇄ hypothesis telugu
• తెలుగు సంస్కృతము లోనుంచి పుట్టినదని వూహించడమును నేను వొప్పలేదు. ⇄ hypothesis telugu
• పక్షము ⇄ hypothesis telugu
• సందేహము ⇄ hypothesis telugu

hypothesis in English

• hypothesis ⇄ hypothesis , noun, pl.-ses. 1. something assumed because it seems likely to be a true explanation; theory. Ex. Let us act on the hypothesis that he is honest. 2. a proposition assumed as a basis for reasoning; supposition. A theorem in g english

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Hypothesis in punjabi.

hypothesis | Punjabi dictionary translates English to Punjabi and Punjabi to English hypothesis words      hypothesis phrases with hypothesis synonyms hypothesis antonyms    hypothesis pronunciations .

hypothesis meaning in Punjabi

hypothesis in Punjabi    Punjabi of translation of hypothesis    Punjabi meaning of hypothesis    what is hypothesis in Punjabi    dictionary definition, antonym, and synonym of hypothesis

Thesaurus: Synonym & Antonym of hypothesis

Indian Official Languages Dictionary is significantly better than Google translation offers multiple meanings, alternate words list of hypothesis    hypothesis phrases    with similar meanings in Punjabi ਪੰਜਾਬੀ, Punjabi ਪੰਜਾਬੀ dictionary    Punjabi ਪੰਜਾਬੀ hypothesis translation    hypothesis meaning    hypothesis definition    hypothesis antonym    hypothesis synonym Punjabi language reference work for finding synonyms,   antonyms of hypothesis .

This page is an online lexical resource, contains a list of the hypothesis like words    in a Punjabi language in the order of the alphabets, and that tells you what they mean, in the same or other languages including English.

What is 'hypothesis' meaning in Punjabi?

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Scientific Hypotheses: Writing, Promoting, and Predicting Implications

Armen yuri gasparyan.

1 Departments of Rheumatology and Research and Development, Dudley Group NHS Foundation Trust (Teaching Trust of the University of Birmingham, UK), Russells Hall Hospital, Dudley, West Midlands, UK.

Lilit Ayvazyan

2 Department of Medical Chemistry, Yerevan State Medical University, Yerevan, Armenia.

Ulzhan Mukanova

3 Department of Surgical Disciplines, South Kazakhstan Medical Academy, Shymkent, Kazakhstan.

Marlen Yessirkepov

4 Department of Biology and Biochemistry, South Kazakhstan Medical Academy, Shymkent, Kazakhstan.

George D. Kitas

5 Arthritis Research UK Epidemiology Unit, University of Manchester, Manchester, UK.

Scientific hypotheses are essential for progress in rapidly developing academic disciplines. Proposing new ideas and hypotheses require thorough analyses of evidence-based data and predictions of the implications. One of the main concerns relates to the ethical implications of the generated hypotheses. The authors may need to outline potential benefits and limitations of their suggestions and target widely visible publication outlets to ignite discussion by experts and start testing the hypotheses. Not many publication outlets are currently welcoming hypotheses and unconventional ideas that may open gates to criticism and conservative remarks. A few scholarly journals guide the authors on how to structure hypotheses. Reflecting on general and specific issues around the subject matter is often recommended for drafting a well-structured hypothesis article. An analysis of influential hypotheses, presented in this article, particularly Strachan's hygiene hypothesis with global implications in the field of immunology and allergy, points to the need for properly interpreting and testing new suggestions. Envisaging the ethical implications of the hypotheses should be considered both by authors and journal editors during the writing and publishing process.

INTRODUCTION

We live in times of digitization that radically changes scientific research, reporting, and publishing strategies. Researchers all over the world are overwhelmed with processing large volumes of information and searching through numerous online platforms, all of which make the whole process of scholarly analysis and synthesis complex and sophisticated.

Current research activities are diversifying to combine scientific observations with analysis of facts recorded by scholars from various professional backgrounds. 1 Citation analyses and networking on social media are also becoming essential for shaping research and publishing strategies globally. 2 Learning specifics of increasingly interdisciplinary research studies and acquiring information facilitation skills aid researchers in formulating innovative ideas and predicting developments in interrelated scientific fields.

Arguably, researchers are currently offered more opportunities than in the past for generating new ideas by performing their routine laboratory activities, observing individual cases and unusual developments, and critically analyzing published scientific facts. What they need at the start of their research is to formulate a scientific hypothesis that revisits conventional theories, real-world processes, and related evidence to propose new studies and test ideas in an ethical way. 3 Such a hypothesis can be of most benefit if published in an ethical journal with wide visibility and exposure to relevant online databases and promotion platforms.

Although hypotheses are crucially important for the scientific progress, only few highly skilled researchers formulate and eventually publish their innovative ideas per se . Understandably, in an increasingly competitive research environment, most authors would prefer to prioritize their ideas by discussing and conducting tests in their own laboratories or clinical departments, and publishing research reports afterwards. However, there are instances when simple observations and research studies in a single center are not capable of explaining and testing new groundbreaking ideas. Formulating hypothesis articles first and calling for multicenter and interdisciplinary research can be a solution in such instances, potentially launching influential scientific directions, if not academic disciplines.

The aim of this article is to overview the importance and implications of infrequently published scientific hypotheses that may open new avenues of thinking and research.

Despite the seemingly established views on innovative ideas and hypotheses as essential research tools, no structured definition exists to tag the term and systematically track related articles. In 1973, the Medical Subject Heading (MeSH) of the U.S. National Library of Medicine introduced “Research Design” as a structured keyword that referred to the importance of collecting data and properly testing hypotheses, and indirectly linked the term to ethics, methods and standards, among many other subheadings.

One of the experts in the field defines “hypothesis” as a well-argued analysis of available evidence to provide a realistic (scientific) explanation of existing facts, fill gaps in public understanding of sophisticated processes, and propose a new theory or a test. 4 A hypothesis can be proven wrong partially or entirely. However, even such an erroneous hypothesis may influence progress in science by initiating professional debates that help generate more realistic ideas. The main ethical requirement for hypothesis authors is to be honest about the limitations of their suggestions. 5

EXAMPLES OF INFLUENTIAL SCIENTIFIC HYPOTHESES

Daily routine in a research laboratory may lead to groundbreaking discoveries provided the daily accounts are comprehensively analyzed and reproduced by peers. The discovery of penicillin by Sir Alexander Fleming (1928) can be viewed as a prime example of such discoveries that introduced therapies to treat staphylococcal and streptococcal infections and modulate blood coagulation. 6 , 7 Penicillin got worldwide recognition due to the inventor's seminal works published by highly prestigious and widely visible British journals, effective ‘real-world’ antibiotic therapy of pneumonia and wounds during World War II, and euphoric media coverage. 8 In 1945, Fleming, Florey and Chain got a much deserved Nobel Prize in Physiology or Medicine for the discovery that led to the mass production of the wonder drug in the U.S. and ‘real-world practice’ that tested the use of penicillin. What remained globally unnoticed is that Zinaida Yermolyeva, the outstanding Soviet microbiologist, created the Soviet penicillin, which turned out to be more effective than the Anglo-American penicillin and entered mass production in 1943; that year marked the turning of the tide of the Great Patriotic War. 9 One of the reasons of the widely unnoticed discovery of Zinaida Yermolyeva is that her works were published exclusively by local Russian (Soviet) journals.

The past decades have been marked by an unprecedented growth of multicenter and global research studies involving hundreds and thousands of human subjects. This trend is shaped by an increasing number of reports on clinical trials and large cohort studies that create a strong evidence base for practice recommendations. Mega-studies may help generate and test large-scale hypotheses aiming to solve health issues globally. Properly designed epidemiological studies, for example, may introduce clarity to the hygiene hypothesis that was originally proposed by David Strachan in 1989. 10 David Strachan studied the epidemiology of hay fever in a cohort of 17,414 British children and concluded that declining family size and improved personal hygiene had reduced the chances of cross infections in families, resulting in epidemics of atopic disease in post-industrial Britain. Over the past four decades, several related hypotheses have been proposed to expand the potential role of symbiotic microorganisms and parasites in the development of human physiological immune responses early in life and protection from allergic and autoimmune diseases later on. 11 , 12 Given the popularity and the scientific importance of the hygiene hypothesis, it was introduced as a MeSH term in 2012. 13

Hypotheses can be proposed based on an analysis of recorded historic events that resulted in mass migrations and spreading of certain genetic diseases. As a prime example, familial Mediterranean fever (FMF), the prototype periodic fever syndrome, is believed to spread from Mesopotamia to the Mediterranean region and all over Europe due to migrations and religious prosecutions millennia ago. 14 Genetic mutations spearing mild clinical forms of FMF are hypothesized to emerge and persist in the Mediterranean region as protective factors against more serious infectious diseases, particularly tuberculosis, historically common in that part of the world. 15 The speculations over the advantages of carrying the MEditerranean FeVer (MEFV) gene are further strengthened by recorded low mortality rates from tuberculosis among FMF patients of different nationalities living in Tunisia in the first half of the 20th century. 16

Diagnostic hypotheses shedding light on peculiarities of diseases throughout the history of mankind can be formulated using artefacts, particularly historic paintings. 17 Such paintings may reveal joint deformities and disfigurements due to rheumatic diseases in individual subjects. A series of paintings with similar signs of pathological conditions interpreted in a historic context may uncover mysteries of epidemics of certain diseases, which is the case with Ruben's paintings depicting signs of rheumatic hands and making some doctors to believe that rheumatoid arthritis was common in Europe in the 16th and 17th century. 18

WRITING SCIENTIFIC HYPOTHESES

There are author instructions of a few journals that specifically guide how to structure, format, and make submissions categorized as hypotheses attractive. One of the examples is presented by Med Hypotheses , the flagship journal in its field with more than four decades of publishing and influencing hypothesis authors globally. However, such guidance is not based on widely discussed, implemented, and approved reporting standards, which are becoming mandatory for all scholarly journals.

Generating new ideas and scientific hypotheses is a sophisticated task since not all researchers and authors are skilled to plan, conduct, and interpret various research studies. Some experience with formulating focused research questions and strong working hypotheses of original research studies is definitely helpful for advancing critical appraisal skills. However, aspiring authors of scientific hypotheses may need something different, which is more related to discerning scientific facts, pooling homogenous data from primary research works, and synthesizing new information in a systematic way by analyzing similar sets of articles. To some extent, this activity is reminiscent of writing narrative and systematic reviews. As in the case of reviews, scientific hypotheses need to be formulated on the basis of comprehensive search strategies to retrieve all available studies on the topics of interest and then synthesize new information selectively referring to the most relevant items. One of the main differences between scientific hypothesis and review articles relates to the volume of supportive literature sources ( Table 1 ). In fact, hypothesis is usually formulated by referring to a few scientific facts or compelling evidence derived from a handful of literature sources. 19 By contrast, reviews require analyses of a large number of published documents retrieved from several well-organized and evidence-based databases in accordance with predefined search strategies. 20 , 21 , 22

The format of hypotheses, especially the implications part, may vary widely across disciplines. Clinicians may limit their suggestions to the clinical manifestations of diseases, outcomes, and management strategies. Basic and laboratory scientists analysing genetic, molecular, and biochemical mechanisms may need to view beyond the frames of their narrow fields and predict social and population-based implications of the proposed ideas. 23

Advanced writing skills are essential for presenting an interesting theoretical article which appeals to the global readership. Merely listing opposing facts and ideas, without proper interpretation and analysis, may distract the experienced readers. The essence of a great hypothesis is a story behind the scientific facts and evidence-based data.

ETHICAL IMPLICATIONS

The authors of hypotheses substantiate their arguments by referring to and discerning rational points from published articles that might be overlooked by others. Their arguments may contradict the established theories and practices, and pose global ethical issues, particularly when more or less efficient medical technologies and public health interventions are devalued. The ethical issues may arise primarily because of the careless references to articles with low priorities, inadequate and apparently unethical methodologies, and concealed reporting of negative results. 24 , 25

Misinterpretation and misunderstanding of the published ideas and scientific hypotheses may complicate the issue further. For example, Alexander Fleming, whose innovative ideas of penicillin use to kill susceptible bacteria saved millions of lives, warned of the consequences of uncontrolled prescription of the drug. The issue of antibiotic resistance had emerged within the first ten years of penicillin use on a global scale due to the overprescription that affected the efficacy of antibiotic therapies, with undesirable consequences for millions. 26

The misunderstanding of the hygiene hypothesis that primarily aimed to shed light on the role of the microbiome in allergic and autoimmune diseases resulted in decline of public confidence in hygiene with dire societal implications, forcing some experts to abandon the original idea. 27 , 28 Although that hypothesis is unrelated to the issue of vaccinations, the public misunderstanding has resulted in decline of vaccinations at a time of upsurge of old and new infections.

A number of ethical issues are posed by the denial of the viral (human immunodeficiency viruses; HIV) hypothesis of acquired Immune deficiency Syndrome (AIDS) by Peter Duesberg, who overviewed the links between illicit recreational drugs and antiretroviral therapies with AIDS and refuted the etiological role of HIV. 29 That controversial hypothesis was rejected by several journals, but was eventually published without external peer review at Med Hypotheses in 2010. The publication itself raised concerns of the unconventional editorial policy of the journal, causing major perturbations and more scrutinized publishing policies by journals processing hypotheses.

WHERE TO PUBLISH HYPOTHESES

Although scientific authors are currently well informed and equipped with search tools to draft evidence-based hypotheses, there are still limited quality publication outlets calling for related articles. The journal editors may be hesitant to publish articles that do not adhere to any research reporting guidelines and open gates for harsh criticism of unconventional and untested ideas. Occasionally, the editors opting for open-access publishing and upgrading their ethics regulations launch a section to selectively publish scientific hypotheses attractive to the experienced readers. 30 However, the absence of approved standards for this article type, particularly no mandate for outlining potential ethical implications, may lead to publication of potentially harmful ideas in an attractive format.

A suggestion of simultaneously publishing multiple or alternative hypotheses to balance the reader views and feedback is a potential solution for the mainstream scholarly journals. 31 However, that option alone is hardly applicable to emerging journals with unconventional quality checks and peer review, accumulating papers with multiple rejections by established journals.

A large group of experts view hypotheses with improbable and controversial ideas publishable after formal editorial (in-house) checks to preserve the authors' genuine ideas and avoid conservative amendments imposed by external peer reviewers. 32 That approach may be acceptable for established publishers with large teams of experienced editors. However, the same approach can lead to dire consequences if employed by nonselective start-up, open-access journals processing all types of articles and primarily accepting those with charged publication fees. 33 In fact, pseudoscientific ideas arguing Newton's and Einstein's seminal works or those denying climate change that are hardly testable have already found their niche in substandard electronic journals with soft or nonexistent peer review. 34

CITATIONS AND SOCIAL MEDIA ATTENTION

The available preliminary evidence points to the attractiveness of hypothesis articles for readers, particularly those from research-intensive countries who actively download related documents. 35 However, citations of such articles are disproportionately low. Only a small proportion of top-downloaded hypotheses (13%) in the highly prestigious Med Hypotheses receive on average 5 citations per article within a two-year window. 36

With the exception of a few historic papers, the vast majority of hypotheses attract relatively small number of citations in a long term. 36 Plausible explanations are that these articles often contain a single or only a few citable points and that suggested research studies to test hypotheses are rarely conducted and reported, limiting chances of citing and crediting authors of genuine research ideas.

A snapshot analysis of citation activity of hypothesis articles may reveal interest of the global scientific community towards their implications across various disciplines and countries. As a prime example, Strachan's hygiene hypothesis, published in 1989, 10 is still attracting numerous citations on Scopus, the largest bibliographic database. As of August 28, 2019, the number of the linked citations in the database is 3,201. Of the citing articles, 160 are cited at least 160 times ( h -index of this research topic = 160). The first three citations are recorded in 1992 and followed by a rapid annual increase in citation activity and a peak of 212 in 2015 ( Fig. 1 ). The top 5 sources of the citations are Clin Exp Allergy (n = 136), J Allergy Clin Immunol (n = 119), Allergy (n = 81), Pediatr Allergy Immunol (n = 69), and PLOS One (n = 44). The top 5 citing authors are leading experts in pediatrics and allergology Erika von Mutius (Munich, Germany, number of publications with the index citation = 30), Erika Isolauri (Turku, Finland, n = 27), Patrick G Holt (Subiaco, Australia, n = 25), David P. Strachan (London, UK, n = 23), and Bengt Björksten (Stockholm, Sweden, n = 22). The U.S. is the leading country in terms of citation activity with 809 related documents, followed by the UK (n = 494), Germany (n = 314), Australia (n = 211), and the Netherlands (n = 177). The largest proportion of citing documents are articles (n = 1,726, 54%), followed by reviews (n = 950, 29.7%), and book chapters (n = 213, 6.7%). The main subject areas of the citing items are medicine (n = 2,581, 51.7%), immunology and microbiology (n = 1,179, 23.6%), and biochemistry, genetics and molecular biology (n = 415, 8.3%).

Interestingly, a recent analysis of 111 publications related to Strachan's hygiene hypothesis, stating that the lack of exposure to infections in early life increases the risk of rhinitis, revealed a selection bias of 5,551 citations on Web of Science. 37 The articles supportive of the hypothesis were cited more than nonsupportive ones (odds ratio adjusted for study design, 2.2; 95% confidence interval, 1.6–3.1). A similar conclusion pointing to a citation bias distorting bibliometrics of hypotheses was reached by an earlier analysis of a citation network linked to the idea that β-amyloid, which is involved in the pathogenesis of Alzheimer disease, is produced by skeletal muscle of patients with inclusion body myositis. 38 The results of both studies are in line with the notion that ‘positive’ citations are more frequent in the field of biomedicine than ‘negative’ ones, and that citations to articles with proven hypotheses are too common. 39

Social media channels are playing an increasingly active role in the generation and evaluation of scientific hypotheses. In fact, publicly discussing research questions on platforms of news outlets, such as Reddit, may shape hypotheses on health-related issues of global importance, such as obesity. 40 Analyzing Twitter comments, researchers may reveal both potentially valuable ideas and unfounded claims that surround groundbreaking research ideas. 41 Social media activities, however, are unevenly distributed across different research topics, journals and countries, and these are not always objective professional reflections of the breakthroughs in science. 2 , 42

Scientific hypotheses are essential for progress in science and advances in healthcare. Innovative ideas should be based on a critical overview of related scientific facts and evidence-based data, often overlooked by others. To generate realistic hypothetical theories, the authors should comprehensively analyze the literature and suggest relevant and ethically sound design for future studies. They should also consider their hypotheses in the context of research and publication ethics norms acceptable for their target journals. The journal editors aiming to diversify their portfolio by maintaining and introducing hypotheses section are in a position to upgrade guidelines for related articles by pointing to general and specific analyses of the subject, preferred study designs to test hypotheses, and ethical implications. The latter is closely related to specifics of hypotheses. For example, editorial recommendations to outline benefits and risks of a new laboratory test or therapy may result in a more balanced article and minimize associated risks afterwards.

Not all scientific hypotheses have immediate positive effects. Some, if not most, are never tested in properly designed research studies and never cited in credible and indexed publication outlets. Hypotheses in specialized scientific fields, particularly those hardly understandable for nonexperts, lose their attractiveness for increasingly interdisciplinary audience. The authors' honest analysis of the benefits and limitations of their hypotheses and concerted efforts of all stakeholders in science communication to initiate public discussion on widely visible platforms and social media may reveal rational points and caveats of the new ideas.

Disclosure: The authors have no potential conflicts of interest to disclose.

Author Contributions:

• Conceptualization: Gasparyan AY, Yessirkepov M, Kitas GD.
• Methodology: Gasparyan AY, Mukanova U, Ayvazyan L.
• Writing - original draft: Gasparyan AY, Ayvazyan L, Yessirkepov M.
• Writing - review & editing: Gasparyan AY, Yessirkepov M, Mukanova U, Kitas GD.

Research Hypothesis In Psychology: Types, & Examples

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

A research hypothesis, in its plural form “hypotheses,” is a specific, testable prediction about the anticipated results of a study, established at its outset. It is a key component of the scientific method .

Hypotheses connect theory to data and guide the research process towards expanding scientific understanding

Some key points about hypotheses:

• A hypothesis expresses an expected pattern or relationship. It connects the variables under investigation.
• It is stated in clear, precise terms before any data collection or analysis occurs. This makes the hypothesis testable.
• A hypothesis must be falsifiable. It should be possible, even if unlikely in practice, to collect data that disconfirms rather than supports the hypothesis.
• Hypotheses guide research. Scientists design studies to explicitly evaluate hypotheses about how nature works.
• For a hypothesis to be valid, it must be testable against empirical evidence. The evidence can then confirm or disprove the testable predictions.
• Hypotheses are informed by background knowledge and observation, but go beyond what is already known to propose an explanation of how or why something occurs.

Predictions typically arise from a thorough knowledge of the research literature, curiosity about real-world problems or implications, and integrating this to advance theory. They build on existing literature while providing new insight.

Types of Research Hypotheses

Alternative hypothesis.

The research hypothesis is often called the alternative or experimental hypothesis in experimental research.

It typically suggests a potential relationship between two key variables: the independent variable, which the researcher manipulates, and the dependent variable, which is measured based on those changes.

The alternative hypothesis states a relationship exists between the two variables being studied (one variable affects the other).

A hypothesis is a testable statement or prediction about the relationship between two or more variables. It is a key component of the scientific method. Some key points about hypotheses:

• Important hypotheses lead to predictions that can be tested empirically. The evidence can then confirm or disprove the testable predictions.

In summary, a hypothesis is a precise, testable statement of what researchers expect to happen in a study and why. Hypotheses connect theory to data and guide the research process towards expanding scientific understanding.

An experimental hypothesis predicts what change(s) will occur in the dependent variable when the independent variable is manipulated.

It states that the results are not due to chance and are significant in supporting the theory being investigated.

The alternative hypothesis can be directional, indicating a specific direction of the effect, or non-directional, suggesting a difference without specifying its nature. It’s what researchers aim to support or demonstrate through their study.

Null Hypothesis

The null hypothesis states no relationship exists between the two variables being studied (one variable does not affect the other). There will be no changes in the dependent variable due to manipulating the independent variable.

It states results are due to chance and are not significant in supporting the idea being investigated.

The null hypothesis, positing no effect or relationship, is a foundational contrast to the research hypothesis in scientific inquiry. It establishes a baseline for statistical testing, promoting objectivity by initiating research from a neutral stance.

Many statistical methods are tailored to test the null hypothesis, determining the likelihood of observed results if no true effect exists.

This dual-hypothesis approach provides clarity, ensuring that research intentions are explicit, and fosters consistency across scientific studies, enhancing the standardization and interpretability of research outcomes.

Nondirectional Hypothesis

A non-directional hypothesis, also known as a two-tailed hypothesis, predicts that there is a difference or relationship between two variables but does not specify the direction of this relationship.

It merely indicates that a change or effect will occur without predicting which group will have higher or lower values.

For example, “There is a difference in performance between Group A and Group B” is a non-directional hypothesis.

Directional Hypothesis

A directional (one-tailed) hypothesis predicts the nature of the effect of the independent variable on the dependent variable. It predicts in which direction the change will take place. (i.e., greater, smaller, less, more)

It specifies whether one variable is greater, lesser, or different from another, rather than just indicating that there’s a difference without specifying its nature.

For example, “Exercise increases weight loss” is a directional hypothesis.

Falsifiability

The Falsification Principle, proposed by Karl Popper , is a way of demarcating science from non-science. It suggests that for a theory or hypothesis to be considered scientific, it must be testable and irrefutable.

Falsifiability emphasizes that scientific claims shouldn’t just be confirmable but should also have the potential to be proven wrong.

It means that there should exist some potential evidence or experiment that could prove the proposition false.

However many confirming instances exist for a theory, it only takes one counter observation to falsify it. For example, the hypothesis that “all swans are white,” can be falsified by observing a black swan.

For Popper, science should attempt to disprove a theory rather than attempt to continually provide evidence to support a research hypothesis.

Can a Hypothesis be Proven?

Hypotheses make probabilistic predictions. They state the expected outcome if a particular relationship exists. However, a study result supporting a hypothesis does not definitively prove it is true.

All studies have limitations. There may be unknown confounding factors or issues that limit the certainty of conclusions. Additional studies may yield different results.

In science, hypotheses can realistically only be supported with some degree of confidence, not proven. The process of science is to incrementally accumulate evidence for and against hypothesized relationships in an ongoing pursuit of better models and explanations that best fit the empirical data. But hypotheses remain open to revision and rejection if that is where the evidence leads.

• Disproving a hypothesis is definitive. Solid disconfirmatory evidence will falsify a hypothesis and require altering or discarding it based on the evidence.
• However, confirming evidence is always open to revision. Other explanations may account for the same results, and additional or contradictory evidence may emerge over time.

We can never 100% prove the alternative hypothesis. Instead, we see if we can disprove, or reject the null hypothesis.

If we reject the null hypothesis, this doesn’t mean that our alternative hypothesis is correct but does support the alternative/experimental hypothesis.

Upon analysis of the results, an alternative hypothesis can be rejected or supported, but it can never be proven to be correct. We must avoid any reference to results proving a theory as this implies 100% certainty, and there is always a chance that evidence may exist which could refute a theory.

How to Write a Hypothesis

• Identify variables . The researcher manipulates the independent variable and the dependent variable is the measured outcome.
• Operationalized the variables being investigated . Operationalization of a hypothesis refers to the process of making the variables physically measurable or testable, e.g. if you are about to study aggression, you might count the number of punches given by participants.
• Decide on a direction for your prediction . If there is evidence in the literature to support a specific effect of the independent variable on the dependent variable, write a directional (one-tailed) hypothesis. If there are limited or ambiguous findings in the literature regarding the effect of the independent variable on the dependent variable, write a non-directional (two-tailed) hypothesis.
• Make it Testable : Ensure your hypothesis can be tested through experimentation or observation. It should be possible to prove it false (principle of falsifiability).
• Clear & concise language . A strong hypothesis is concise (typically one to two sentences long), and formulated using clear and straightforward language, ensuring it’s easily understood and testable.

Consider a hypothesis many teachers might subscribe to: students work better on Monday morning than on Friday afternoon (IV=Day, DV= Standard of work).

Now, if we decide to study this by giving the same group of students a lesson on a Monday morning and a Friday afternoon and then measuring their immediate recall of the material covered in each session, we would end up with the following:

• The alternative hypothesis states that students will recall significantly more information on a Monday morning than on a Friday afternoon.
• The null hypothesis states that there will be no significant difference in the amount recalled on a Monday morning compared to a Friday afternoon. Any difference will be due to chance or confounding factors.

More Examples

• Memory : Participants exposed to classical music during study sessions will recall more items from a list than those who studied in silence.
• Social Psychology : Individuals who frequently engage in social media use will report higher levels of perceived social isolation compared to those who use it infrequently.
• Developmental Psychology : Children who engage in regular imaginative play have better problem-solving skills than those who don’t.
• Clinical Psychology : Cognitive-behavioral therapy will be more effective in reducing symptoms of anxiety over a 6-month period compared to traditional talk therapy.
• Cognitive Psychology : Individuals who multitask between various electronic devices will have shorter attention spans on focused tasks than those who single-task.
• Health Psychology : Patients who practice mindfulness meditation will experience lower levels of chronic pain compared to those who don’t meditate.
• Organizational Psychology : Employees in open-plan offices will report higher levels of stress than those in private offices.
• Behavioral Psychology : Rats rewarded with food after pressing a lever will press it more frequently than rats who receive no reward.

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Definition of hypothesis

Did you know.

The Difference Between Hypothesis and Theory

A hypothesis is an assumption, an idea that is proposed for the sake of argument so that it can be tested to see if it might be true.

In the scientific method, the hypothesis is constructed before any applicable research has been done, apart from a basic background review. You ask a question, read up on what has been studied before, and then form a hypothesis.

A hypothesis is usually tentative; it's an assumption or suggestion made strictly for the objective of being tested.

A theory , in contrast, is a principle that has been formed as an attempt to explain things that have already been substantiated by data. It is used in the names of a number of principles accepted in the scientific community, such as the Big Bang Theory . Because of the rigors of experimentation and control, it is understood to be more likely to be true than a hypothesis is.

In non-scientific use, however, hypothesis and theory are often used interchangeably to mean simply an idea, speculation, or hunch, with theory being the more common choice.

Since this casual use does away with the distinctions upheld by the scientific community, hypothesis and theory are prone to being wrongly interpreted even when they are encountered in scientific contexts—or at least, contexts that allude to scientific study without making the critical distinction that scientists employ when weighing hypotheses and theories.

The most common occurrence is when theory is interpreted—and sometimes even gleefully seized upon—to mean something having less truth value than other scientific principles. (The word law applies to principles so firmly established that they are almost never questioned, such as the law of gravity.)

This mistake is one of projection: since we use theory in general to mean something lightly speculated, then it's implied that scientists must be talking about the same level of uncertainty when they use theory to refer to their well-tested and reasoned principles.

The distinction has come to the forefront particularly on occasions when the content of science curricula in schools has been challenged—notably, when a school board in Georgia put stickers on textbooks stating that evolution was "a theory, not a fact, regarding the origin of living things." As Kenneth R. Miller, a cell biologist at Brown University, has said , a theory "doesn’t mean a hunch or a guess. A theory is a system of explanations that ties together a whole bunch of facts. It not only explains those facts, but predicts what you ought to find from other observations and experiments.”

While theories are never completely infallible, they form the basis of scientific reasoning because, as Miller said "to the best of our ability, we’ve tested them, and they’ve held up."

• proposition
• supposition

hypothesis , theory , law mean a formula derived by inference from scientific data that explains a principle operating in nature.

hypothesis implies insufficient evidence to provide more than a tentative explanation.

theory implies a greater range of evidence and greater likelihood of truth.

law implies a statement of order and relation in nature that has been found to be invariable under the same conditions.

Examples of hypothesis in a Sentence

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

Word History

Greek, from hypotithenai to put under, suppose, from hypo- + tithenai to put — more at do

1641, in the meaning defined at sense 1a

Phrases Containing hypothesis

• planetesimal hypothesis
• nebular hypothesis
• counter - hypothesis
• null hypothesis
• Whorfian hypothesis

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hypothesize

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“Hypothesis.” Merriam-Webster.com Dictionary , Merriam-Webster, https://www.merriam-webster.com/dictionary/hypothesis. Accessed 18 Apr. 2024.

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hypothesis meaning of punjabi

Translation of "hypothesis" into panjabi.

ਮਨੌਤ is the translation of "hypothesis" into Panjabi. Sample translated sentence: These findings led psychologist Carol Tavris to write: “It is time to put a bullet, once and for all, through [the] heart of the catharsis hypothesis. ↔ ਇਨ੍ਹਾਂ ਖੋਜਾਂ ਕਰਕੇ ਮਨੋਵਿਗਿਆਨੀ ਕੈਰਲ ਟੈਵਰਸ ਨੇ ਲਿਖਿਆ: “ਹੁਣ ਸਾਨੂੰ ਕਥਾਰਸਿਸ ਦੀ ਥਿਊਰੀ ਨੂੰ ਗੋਲੀ ਮਾਰ ਦੇਣੀ ਚਾਹੀਦੀ ਹੈ।

(sciences) A tentative conjecture explaining an observation, phenomenon or scientific problem that can be tested by further observation, investigation and/or experimentation. [..]

English-Panjabi dictionary

proposed explanation for a phenomenon

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Automatic translations of " hypothesis " into Panjabi

Translations of "hypothesis" into panjabi in sentences, translation memory.

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English to Punjabi Meaning of hypothesis - ਪਰਿਕਲਪਨਾ

ਪਰਿਕਲਪਨਾ, ਰਵ, ਅਨੁਿਾਨ, ਮੰਨਦੇ

Let's say for a moment that I accept the bath-item-gift HYPOTHESIS .

...on your tremendously exciting and not yet conclusively disproved HYPOTHESIS .

Interesting HYPOTHESIS .

Okay. All right. Let's assume your HYPOTHESIS .

Well, that's certainly a thought-provoking HYPOTHESIS .

Meaning and definitions of hypothesis, translation in Punjabi language for hypothesis with similar and opposite words. Also find spoken pronunciation of hypothesis in Punjabi and in English language.

What hypothesis means in Punjabi, hypothesis meaning in Punjabi, hypothesis definition, examples and pronunciation of hypothesis in Punjabi language.

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Hypothesis Meaning In Punjabi

ਸਧਾਰਨ ਉਦਾਹਰਣਾਂ ਅਤੇ ਪਰਿਭਾਸ਼ਾਵਾਂ ਦੇ ਨਾਲ hypothesis ਦਾ ਅਸਲ ਅਰਥ ਜਾਣੋ।., ਪਰਿਭਾਸ਼ਾਵਾਂ, definitions of hypothesis.

1 . ਅੱਗੇ ਦੀ ਜਾਂਚ ਲਈ ਸ਼ੁਰੂਆਤੀ ਬਿੰਦੂ ਵਜੋਂ ਸੀਮਤ ਸਬੂਤਾਂ ਦੇ ਆਧਾਰ 'ਤੇ ਪੇਸ਼ ਕੀਤੀ ਗਈ ਪਰਿਕਲਪਨਾ ਜਾਂ ਸਪੱਸ਼ਟੀਕਰਨ।

1 . a supposition or proposed explanation made on the basis of limited evidence as a starting point for further investigation.

ਸਮਾਨਾਰਥੀ ਸ਼ਬਦ

Examples of hypothesis :.

1 . ਪੈਨਸਪਰਮੀਆ ਪਰਿਕਲਪਨਾ ਵਿਕਲਪਿਕ ਤੌਰ 'ਤੇ ਸੁਝਾਅ ਦਿੰਦੀ ਹੈ ਕਿ ਮਾਈਕ੍ਰੋਸਕੋਪਿਕ ਜੀਵਨ ਨੂੰ ਸ਼ੁਰੂਆਤੀ ਧਰਤੀ 'ਤੇ meteorites, asteroids ਅਤੇ ਹੋਰ ਛੋਟੇ ਸੂਰਜੀ ਸਿਸਟਮ ਦੇ ਸਰੀਰ ਦੁਆਰਾ ਵੰਡਿਆ ਗਿਆ ਸੀ ਅਤੇ ਇਹ ਜੀਵਨ ਪੂਰੇ ਬ੍ਰਹਿਮੰਡ ਵਿੱਚ ਮੌਜੂਦ ਹੋ ਸਕਦਾ ਹੈ।

1 . the panspermia hypothesis alternatively suggests that microscopic life was distributed to the early earth by meteoroids, asteroids and other small solar system bodies and that life may exist throughout the universe.

2 . ਨਿਰੰਤਰ ਪਰਿਕਲਪਨਾ

2 . the continuum hypothesis .

3 . ਇੱਕ ਅਪ੍ਰਮਾਣਿਤ ਪਰਿਕਲਪਨਾ

3 . an unverifiable hypothesis

4 . ਨਲ ਪਰਿਕਲਪਨਾ ਟੈਸਟਿੰਗ ਮਹੱਤਵਪੂਰਨ ਹੈ.

4 . Null hypothesis testing is important.

5 . ਹਰੇਕ ਪਰਿਕਲਪਨਾ, ਜੋ ਕਿਸੇ ਇੱਕ ਓਨਟੋਲੋਜੀਕਲ ਸਿਧਾਂਤਾਂ ਦਾ ਖੰਡਨ ਕਰਦੀ ਹੈ, ਗਲਤ ਹੈ।

5 . Each hypothesis , which contradicts one of the ontological principles, is wrong.

6 . ਪੈਨਸਪਰਮੀਆ ਪਰਿਕਲਪਨਾ ਇਹ ਦਰਸਾਉਂਦੀ ਹੈ ਕਿ ਸੂਖਮ ਜੀਵਨ ਨੂੰ ਸ਼ੁਰੂਆਤੀ ਧਰਤੀ 'ਤੇ ਪੁਲਾੜ ਦੀ ਧੂੜ, ਮੀਟੋਰਾਈਟਸ, ਐਸਟੋਰਾਇਡਜ਼ ਅਤੇ ਹੋਰ ਛੋਟੇ ਸੂਰਜੀ ਸਿਸਟਮਾਂ ਦੁਆਰਾ ਵੰਡਿਆ ਗਿਆ ਸੀ ਅਤੇ ਇਹ ਜੀਵਨ ਪੂਰੇ ਬ੍ਰਹਿਮੰਡ ਵਿੱਚ ਮੌਜੂਦ ਹੋ ਸਕਦਾ ਹੈ।

6 . the panspermia hypothesis suggests that microscopic life was distributed to the early earth by space dust, meteoroids, asteroids and other small solar system bodies and that life may exist throughout the universe.

7 . ਇੱਕ ਪਰੀਖਣਯੋਗ ਪਰਿਕਲਪਨਾ

7 . a testable hypothesis

8 . ਫੈਂਟਮ ਟਾਈਮ ਕਲਪਨਾ.

8 . phantom time hypothesis .

9 . ਇਸ ਧਾਰਨਾ ਨੂੰ ਪਰਖਣ ਲਈ, ਡਾ.

9 . to test this hypothesis , dr.

10 . ਮੈਂ ਆਪਣੀ ਧਾਰਨਾ ਵਿੱਚ ਸਹੀ ਸੀ।

10 . i was correct in my hypothesis .

11 . ਖੁਸ਼ੀ ਦੀ ਕਲਪਨਾ" ਹੈਡਟ.

11 . the happiness hypothesis " haidt.

12 . ਇਹ ਢਲਾਨ ਇੱਕ ਨਵੀਂ ਪਰਿਕਲਪਨਾ ਨੂੰ ਉਭਾਰਦਾ ਹੈ।

12 . this slope raises a new hypothesis .

13 . ਹਾਈਪੋਥੀਸਿਸ ਬੀ: ਇਤਿਹਾਸ ਦਾ ਇੱਕ ਅਰਥ ਹੁੰਦਾ ਹੈ।

13 . Hypothesis B: history has a meaning.

14 . ਇਹ sapir-whorf ਪਰਿਕਲਪਨਾ ਨਾਲ ਮੇਲ ਖਾਂਦਾ ਹੈ।

14 . this corresponds to the sapir-whorf hypothesis .

15 . ਮੇਰੇ ਕੋਲ ਇੱਕ ਅਨੁਮਾਨ ਹੈ ਜੋ ਸਹੀ ਹੋ ਸਕਦਾ ਹੈ ਜਾਂ ਨਹੀਂ।

15 . i have a hypothesis that may or may not be right.

16 . ਪਰਿਕਲਪਨਾ ਸਿਰਫ਼ ਅਪ੍ਰਵਾਨਯੋਗ ਨਹੀਂ ਹੈ, ਪਰ ਝੂਠੀ ਹੈ

16 . the hypothesis is not merely unprovable, but false

17 . ਉਸਨੇ ਸਾਰਿਆਂ ਨੂੰ ਸਹੁੰ ਖਾਧੀ ਕਿ ਪਰਿਕਲਪਨਾ ਕੰਮ ਕਰੇਗੀ।

17 . She swore to everyone that the hypothesis would work.

18 . … ਜਾਂ ਪਰਿਕਲਪਨਾ ਨੂੰ ਰੱਦ ਕਰਨ ਲਈ ਇੱਕ "ਬੇਨਤੀ"?

18 . … or a “Requiem” for the rejection of the hypothesis ?

19 . ਤੁਸੀਂ ਇੱਕ ਨਲ ਪਰਿਕਲਪਨਾ ਨੂੰ ਰੱਦ ਕਰਨ ਲਈ ANOVA ਦੀ ਵਰਤੋਂ ਕਿਵੇਂ ਕਰ ਸਕਦੇ ਹੋ?

19 . How can you use the ANOVA to reject a null hypothesis ?

20 . ਬਹੁਤ ਅੱਛਾ; ਤੁਸੀਂ ਇਸ ਹੋਰ ਪਰਿਕਲਪਨਾ ਬਾਰੇ ਕੀ ਕਹਿੰਦੇ ਹੋ?

20 . Very good; what say you then to this other hypothesis ?

Similar Words

Hypothesis meaning in Punjabi - Learn actual meaning of Hypothesis with simple examples & definitions. Also you will learn Antonyms , synonyms & best example sentences. This dictionary also provide you 10 languages so you can find meaning of Hypothesis in Hindi, Tamil , Telugu , Bengali , Kannada , Marathi , Malayalam , Gujarati , Punjabi , Urdu.

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hypothesis Punjabi Meaning

Punjabi Meaning for hypothesis

n. khayal. M; anman. M;

Punjabi Shahmukhi Dictionary

English to Punjabi Shahmukhi Dictionary

Definitions

English definition for hypothesis

1. n. a tentative theory about the natural world; a concept that is not yet verified but that if true would explain certain facts or phenomena

2. n. a message expressing an opinion based on incomplete evidence

3. n. a proposal intended to explain certain facts or observations

Synonyms and Antonyms for hypothesis

Related Images

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International Languages

Meaning for hypothesis found in 56 Languages.

Near By Words

• hypothesist

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Hypothesis meaning in Punjabi

Hypothesis meaning in Punjabi. Here you learn English to Punjabi translation / English to Punjabi dictionary  of the word ' Hypothesis ' and also play  quiz in Punjabi words starting with  H  also play  A-Z dictionary quiz . To learn Punjabi language , common vocabulary and grammar are the important sections. Common Vocabulary contains common words that we can used in daily life. This way to learn Punjabi language quickly and learn  daily use sentences  helps to improve your Punjabi language. If you think too hard to learn Punjabi language, 1000 words will helps to learn Punjabi language easily, they contain 2-letter words to 13-letter words. Below you see how to say Hypothesis in Punjabi.

How to say 'Hypothesis' in Punjabi

ਅਨੁਮਾਨ anumana

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How to Say Hypothesis in Punjabi

• hypoglycemia
• hypothermia
• hypothesize
• hypothetical
• hypothetically
• at the outset
• first of all
• strict control

Singh Glosbe

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• Morphology of Persian loan nouns in Punjabi by Mehwish Noor, Riaz Ahmed Mangrio, Behzad Anwar, in Pakistan Journal of social issues (2019)

• Les mouvements pour le panjabi à Lahore entre 1947 et 1960 (Pakistan) by Julien-Régis Columeau, thesis (2019)

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• Panjabi manual and grammar : a guide to the colloquial Panjabi , by Thomas Grahame Bailey (1912)

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manuscript in Punjabi

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ਸਾਰਾ ਮਨੁੱਖੀ ਪਰਿਵਾਰ ਆਪਣੀ ਮਹਿਮਾ, ਸ਼ਾਨ ਅਤੇ ਹੱਕਾਂ ਦੇ ਪੱਖੋਂ ਜਨਮ ਤੋਂ ਹੀ ਆਜ਼ਾਦ ਹੈ ਅਤੇ ਸੁਤੇ ਸਿੱਧ ਸਾਰੇ ਲੋਕ ਬਰਾਬਰ ਹਨ । ਉਨ੍ਹਾਂ ਸਭਨਾ ਨੂੰ ਤਰਕ ਅਤੇ ਜ਼ਮੀਰ ਦੀ ਸੌਗਾਤ ਮਿਲੀ ਹੋਈ ਹੈ ਅਤੇ ਉਨ੍ਹਾਂ ਨੂੰ ਭਰਾਤਰੀਭਾਵ ਦੀ ਭਾਵਨਾ ਰਖਦਿਆਂ ਆਪਸ ਵਿਚ ਵਿਚਰਣਾ ਚਾਹੀਦਾ ਹੈ ।

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hypothesis in Gujarati ગુજરાતી

• ગૃહીત માની લીધેલી–વાત ⇄ hypothesis gujarati
• પૂર્વધારણ ા ⇄ hypothesis gujarati
• પૂર્વસિદ્ધાન્ત ⇄ hypothesis gujarati

hypothesis in Hindi हिन्दी

• परिकल्पना ⇄ hypothesis hindi
• प्रकल्पना ⇄ hypothesis hindi
• प्रमेय ⇄ hypothesis hindi
• प्राक्कल्पना ⇄ hypothesis hindi

hypothesis in Kashmiri कॉशुर

• فَرضی دعوا ⇄ hypothesis kashmiri

hypothesis in Maithili মৈথিলী

• अवधारणा ⇄ Hypothesis maithili

hypothesis in Marathi मराठी

• अभ्युपगम ⇄ hypothesis marathi
• गृहीत ⇄ hypothesis marathi
• गृहीत उपन्यास ⇄ hypothesis marathi
• गृहीत धरलेली गोष्ट ⇄ hypothesis marathi
• गृहीत पक्ष ⇄ hypothesis marathi
• गृहीतक ⇄ hypothesis marathi
• गृहीतकल्पना ⇄ hypothesis marathi
• गृहीतकृत्य ⇄ hypothesis marathi
• पक्ष ⇄ hypothesis marathi
• परिकल्पना ⇄ hypothesis marathi
• प्रमेय ⇄ hypothesis marathi

hypothesis in Santali

• उपकल्पना ⇄ hypothesis sanskrit

hypothesis in Sindhi سنڌي

• مفروضو، قياس، دعويٰ، فرض ⇄ Hypothesis sindhi

hypothesis in Telugu తెలుగు

• అనుమానము ⇄ hypothesis telugu
• ఊహ ⇄ hypothesis telugu
• ఎన్నిక. తెలుగుసంస్కృతములో నుంచి పుట్టినదనే పక్షమును నేను వొప్పలేదు ⇄ hypothesis telugu
• తెలుగు సంస్కృతము లోనుంచి పుట్టినదని వూహించడమును నేను వొప్పలేదు. ⇄ hypothesis telugu
• పక్షము ⇄ hypothesis telugu
• సందేహము ⇄ hypothesis telugu

hypothesis in English

• hypothesis ⇄ hypothesis , noun, pl.-ses. 1. something assumed because it seems likely to be a true explanation; theory. Ex. Let us act on the hypothesis that he is honest. 2. a proposition assumed as a basis for reasoning; supposition. A theorem in g english

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Hypothesis in punjabi.

hypothesis | Punjabi dictionary translates English to Punjabi and Punjabi to English hypothesis words      hypothesis phrases with hypothesis synonyms hypothesis antonyms    hypothesis pronunciations .

hypothesis meaning in Punjabi

hypothesis in Punjabi    Punjabi of translation of hypothesis    Punjabi meaning of hypothesis    what is hypothesis in Punjabi    dictionary definition, antonym, and synonym of hypothesis

Thesaurus: Synonym & Antonym of hypothesis

Indian Official Languages Dictionary is significantly better than Google translation offers multiple meanings, alternate words list of hypothesis    hypothesis phrases    with similar meanings in Punjabi ਪੰਜਾਬੀ, Punjabi ਪੰਜਾਬੀ dictionary    Punjabi ਪੰਜਾਬੀ hypothesis translation    hypothesis meaning    hypothesis definition    hypothesis antonym    hypothesis synonym Punjabi language reference work for finding synonyms,   antonyms of hypothesis .

This page is an online lexical resource, contains a list of the hypothesis like words    in a Punjabi language in the order of the alphabets, and that tells you what they mean, in the same or other languages including English.

What is 'hypothesis' meaning in Punjabi?

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Definition of hypothesis noun from the Oxford Advanced American Dictionary

• formulate/advance a theory/hypothesis
• build/construct/create/develop a simple/theoretical/mathematical model
• develop/establish/provide/use a theoretical/conceptual framework/an algorithm
• 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/calculations
• take/record measurements
• 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 behavior
• 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
• analyze/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

Definitions on the go

Look up any word in the dictionary offline, anytime, anywhere with the Oxford Advanced Learner’s Dictionary app.

• 2 [ uncountable ] guesses and ideas that are not based on certain knowledge synonym speculation It would be pointless to engage in hypothesis before we have the facts.

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Definition of hypothesis – Learner’s Dictionary

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(Definition of hypothesis from the Cambridge Learner's Dictionary © Cambridge University Press)

Translations of hypothesis

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think/ speak highly of someone

to admire or say admiring things about someone

Renowned and celebrated (Words meaning ‘famous’)

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Methodology

• How to Write a Strong Hypothesis | Steps & Examples

How to Write a Strong Hypothesis | Steps & Examples

Published on May 6, 2022 by Shona McCombes . Revised on November 20, 2023.

A hypothesis is a statement that can be tested by scientific research. If you want to test a relationship between two or more variables, you need to write hypotheses before you start your experiment or data collection .

Example: Hypothesis

Daily apple consumption leads to fewer doctor’s visits.

Table of contents

What is a hypothesis, developing a hypothesis (with example), hypothesis examples, other interesting articles, frequently asked questions about writing hypotheses.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess – it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).

Variables in hypotheses

Hypotheses propose a relationship between two or more types of variables .

• An independent variable is something the researcher changes or controls.
• A dependent variable is something the researcher observes and measures.

If there are any control variables , extraneous variables , or confounding variables , be sure to jot those down as you go to minimize the chances that research bias  will affect your results.

In this example, the independent variable is exposure to the sun – the assumed cause . The dependent variable is the level of happiness – the assumed effect .

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Step 1. Ask a question

Writing a hypothesis begins with a research question that you want to answer. The question should be focused, specific, and researchable within the constraints of your project.

Step 2. Do some preliminary research

Your initial answer to the question should be based on what is already known about the topic. Look for theories and previous studies to help you form educated assumptions about what your research will find.

At this stage, you might construct a conceptual framework to ensure that you’re embarking on a relevant topic . This can also help you identify which variables you will study and what you think the relationships are between them. Sometimes, you’ll have to operationalize more complex constructs.

Step 3. Formulate your hypothesis

Now you should have some idea of what you expect to find. Write your initial answer to the question in a clear, concise sentence.

4. Refine your hypothesis

You need to make sure your hypothesis is specific and testable. There are various ways of phrasing a hypothesis, but all the terms you use should have clear definitions, and the hypothesis should contain:

• The relevant variables
• The specific group being studied
• The predicted outcome of the experiment or analysis

5. Phrase your hypothesis in three ways

To identify the variables, you can write a simple prediction in  if…then form. The first part of the sentence states the independent variable and the second part states the dependent variable.

In academic research, hypotheses are more commonly phrased in terms of correlations or effects, where you directly state the predicted relationship between variables.

If you are comparing two groups, the hypothesis can state what difference you expect to find between them.

6. Write a null hypothesis

If your research involves statistical hypothesis testing , you will also have to write a null hypothesis . The null hypothesis is the default position that there is no association between the variables. The null hypothesis is written as H 0 , while the alternative hypothesis is H 1 or H a .

• H 0 : The number of lectures attended by first-year students has no effect on their final exam scores.
• H 1 : The number of lectures attended by first-year students has a positive effect on their final exam scores.

If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.

• Sampling methods
• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

A hypothesis is not just a guess — it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).

Null and alternative hypotheses are used in statistical hypothesis testing . The null hypothesis of a test always predicts no effect or no relationship between variables, while the alternative hypothesis states your research prediction of an effect or relationship.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

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McCombes, S. (2023, November 20). How to Write a Strong Hypothesis | Steps & Examples. Scribbr. Retrieved March 14, 2024, from https://www.scribbr.com/methodology/hypothesis/

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Definition of Punjabi

Examples of punjabi in a sentence.

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

Word History

Hindi & Urdu pañjābī , from pañjābī of Punjab, from Persian panjābī , from Panjāb Punjab

1787, in the meaning defined at sense 1

Dictionary Entries Near Punjabi

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“Punjabi.” Merriam-Webster.com Dictionary , Merriam-Webster, https://www.merriam-webster.com/dictionary/Punjabi. Accessed 16 Mar. 2024.

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Translation of "hypothesis" into Panjabi. ਮਨੌਤ is the translation of "hypothesis" into Panjabi. Sample translated sentence: These findings led psychologist Carol Tavris to write: "It is time to put a bullet, once and for all, through [the] heart of the catharsis hypothesis. ↔ ਇਨ੍ਹਾਂ ਖੋਜਾਂ ਕਰਕੇ ...

What is hypothesis meaning in Punjabi? The word or phrase hypothesis refers to a tentative insight into the natural world; a concept that is not yet verified but that if true would explain certain facts or phenomena, or a message expressing an opinion based on incomplete evidence, or a proposal intended to explain certain facts or observations.

(7) the hypothesis that every event has a cause (8) This allows a test of the hypothesis that central tendency determines typicality. (9) The hypothesis is that the view from the castle will be so spoiled that it will put off visitors. (10) Embedded within the hypothesis will be concepts that will need to be translated into researchable entities.

Definitions and Meaning of hypothesis in English hypothesis noun. a tentative insight into the natural world; a concept that is not yet verified but that if true would explain certain facts or phenomena. Synonyms : possibility, theory. ਥਿਊਰੀ "a scientific hypothesis that survives experimental testing becomes a scientific theory"

Definitions. 1. n. a tentative theory about the natural world; a concept that is not yet verified but that if true would explain certain facts or phenomena. 2. n. a message expressing an opinion based on incomplete evidence. 3. n.

Hypothesis meaning in Punjabi. Here you learn English to Punjabi translation / English to Punjabi dictionary of the word Hypothesis and also play quiz in Punjabi words starting with H also play A-Z dictionary quiz. To learn Punjabi language, common vocabulary and grammar are the important sections. Common Vocabulary contains common words that we can used in daily life.

Hypothesis: Punjabi Meaning: ਪਰੀਕਲਪਨਾ, ਪਰਿਕਲਪਨਾ a tentative theory about the natural world; a concept that is not yet verified but that if true would explain certain facts or phenomena; a scientific hypothesis that survives experimental testing becomes a scientific theory; he proposed a fresh theory of alkalis that later was accepted in chemical practices / a ...

Shabdkosh.com is a free online dictionary that provides English to Punjabi and Punjabi to English translations, along with synonyms, antonyms, examples, and pronunciation. Whether you want to learn a new language, improve your vocabulary, or find the meaning of a word or phrase, Shabdkosh.com can help you with its comprehensive and user-friendly interface.

If you want to know how to say hypothesis in Punjabi, you will find the translation here. We hope this will help you to understand Punjabi better. Here is the translation and the Punjabi word for hypothesis:

The Punjabi is spoken: - in Pakistan by 100 millions speakers. They use the Arabic script. There is no official status: the language of Pakistan is Urdu. - in India by 40 millions speakers. It's the official language of the state of Punjab. They use the Gurmukhi script. It's also the language of the Sikhs.

hypothesis meaning in Punjabi ਪੰਜਾਬੀ is a translation of hypothesis in Punjabi ਪੰਜਾਬੀ dictionary. Click for meanings of hypothesis, including synonyms, antonyms.

Punjabi language, one of the most widely spoken Indo-Aryan languages.The old British spelling "Punjabi" remains in more common general usage than the academically precise "Panjabi." In the early 21st century there were about 30 million speakers of Punjabi in India.It is the official language of the Indian state of Punjab and is one of the languages recognized by the Indian constitution.

Punjabi culture grew out of the settlements along the five rivers (the name Punjab, is derived from two Persian words, Panj meaning "Five" and Âb meaning "Water") which served as an important route to the Near East as early as the ancient Indus Valley civilization, dating back to 3000 BCE. Agriculture has been the major economic feature of the Punjab and has therefore formed the foundation of ...

The Punjabis (Punjabi: پنجابی (); ਪੰਜਾਬੀ (); romanised as Panjābī), are an Indo-Aryan ethnolinguistic group associated with the Punjab region, comprising areas of eastern Pakistan and northwestern India. They generally speak Standard Punjabi or various Punjabi dialects on both sides.. The ethnonym is derived from the term Punjab (Five rivers) in Persian to describe the ...

hypothesis: [noun] an assumption or concession made for the sake of argument. an interpretation of a practical situation or condition taken as the ground for action.

HYPOTHESIS definition: 1. an idea or explanation for something that is based on known facts but has not yet been proved…. Learn more.

The hypothesis predicts that children will perform better on task A than on task B. The results confirmed his hypothesis on the use of modal verbs. These observations appear to support our working hypothesis. a speculative hypothesis concerning the nature of matter; an interesting hypothesis about the development of language

Definition: Hypothesis is an educated guess or proposed explanation for a phenomenon, based on some initial observations or data. It is a tentative statement that can be tested and potentially proven or disproven through further investigation and experimentation. Hypothesis is often used in scientific research to guide the design of experiments ...

1 [countable] an idea or explanation of something that is based on a few known facts but that has not yet been proved to be true or correct synonym theory to formulate/confirm a hypothesis a hypothesis about the function of dreams There is little evidence to support these hypotheses. Topic Collocations Scientific Research theory. formulate/advance a theory/hypothesis

HYPOTHESIS meaning: a suggested explanation for something that has not yet been proved to be true. Learn more.

6. Write a null hypothesis. If your research involves statistical hypothesis testing, you will also have to write a null hypothesis. The null hypothesis is the default position that there is no association between the variables. The null hypothesis is written as H 0, while the alternative hypothesis is H 1 or H a.

The meaning of PUNJABI is an Indo-Aryan language of the Punjab. an Indo-Aryan language of the Punjab; a native or inhabitant of the Punjab region of the northwestern Indian subcontinent… See the full definition

Frequently asked questions

What is the definition of a hypothesis.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess. It should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations, and statistical analysis of data).

Frequently asked questions: Methodology

Quantitative observations involve measuring or counting something and expressing the result in numerical form, while qualitative observations involve describing something in non-numerical terms, such as its appearance, texture, or color.

To make quantitative observations , you need to use instruments that are capable of measuring the quantity you want to observe. For example, you might use a ruler to measure the length of an object or a thermometer to measure its temperature.

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.

Inclusion and exclusion criteria are predominantly used in non-probability sampling . In purposive sampling and snowball sampling , restrictions apply as to who can be included in the sample .

Inclusion and exclusion criteria are typically presented and discussed in the methodology section of your thesis or dissertation .

The purpose of theory-testing mode is to find evidence in order to disprove, refine, or support a theory. As such, generalisability is not the aim of theory-testing mode.

Due to this, the priority of researchers in theory-testing mode is to eliminate alternative causes for relationships between variables . In other words, they prioritise internal validity over external validity , including ecological validity .

Convergent validity shows how much a measure of one construct aligns with other measures of the same or related constructs .

On the other hand, concurrent validity is about how a measure matches up to some known criterion or gold standard, which can be another measure.

Although both types of validity are established by calculating the association or correlation between a test score and another variable , they represent distinct validation methods.

Validity tells you how accurately a method measures what it was designed to measure. There are 4 main types of validity :

• Construct validity : Does the test measure the construct it was designed to measure?
• Face validity : Does the test appear to be suitable for its objectives ?
• Content validity : Does the test cover all relevant parts of the construct it aims to measure.
• Criterion validity : Do the results accurately measure the concrete outcome they are designed to measure?

Criterion validity evaluates how well a test measures the outcome it was designed to measure. An outcome can be, for example, the onset of a disease.

Criterion validity consists of two subtypes depending on the time at which the two measures (the criterion and your test) are obtained:

• Concurrent validity is a validation strategy where the the scores of a test and the criterion are obtained at the same time
• Predictive validity is a validation strategy where the criterion variables are measured after the scores of the test

Attrition refers to participants leaving a study. It always happens to some extent – for example, in randomised control trials for medical research.

Differential attrition occurs when attrition or dropout rates differ systematically between the intervention and the control group . As a result, the characteristics of the participants who drop out differ from the characteristics of those who stay in the study. Because of this, study results may be biased .

Criterion validity and construct validity are both types of measurement validity . In other words, they both show you how accurately a method measures something.

While construct validity is the degree to which a test or other measurement method measures what it claims to measure, criterion validity is the degree to which a test can predictively (in the future) or concurrently (in the present) measure something.

Construct validity is often considered the overarching type of measurement validity . You need to have face validity , content validity , and criterion validity in order to achieve construct validity.

Convergent validity and discriminant validity are both subtypes of construct validity . Together, they help you evaluate whether a test measures the concept it was designed to measure.

• Convergent validity indicates whether a test that is designed to measure a particular construct correlates with other tests that assess the same or similar construct.
• Discriminant validity indicates whether two tests that should not be highly related to each other are indeed not related. This type of validity is also called divergent validity .

You need to assess both in order to demonstrate construct validity. Neither one alone is sufficient for establishing construct validity.

Face validity and content validity are similar in that they both evaluate how suitable the content of a test is. The difference is that face validity is subjective, and assesses content at surface level.

When a test has strong face validity, anyone would agree that the test’s questions appear to measure what they are intended to measure.

For example, looking at a 4th grade math test consisting of problems in which students have to add and multiply, most people would agree that it has strong face validity (i.e., it looks like a math test).

On the other hand, content validity evaluates how well a test represents all the aspects of a topic. Assessing content validity is more systematic and relies on expert evaluation. of each question, analysing whether each one covers the aspects that the test was designed to cover.

A 4th grade math test would have high content validity if it covered all the skills taught in that grade. Experts(in this case, math teachers), would have to evaluate the content validity by comparing the test to the learning objectives.

Content validity shows you how accurately a test or other measurement method taps  into the various aspects of the specific construct you are researching.

In other words, it helps you answer the question: “does the test measure all aspects of the construct I want to measure?” If it does, then the test has high content validity.

The higher the content validity, the more accurate the measurement of the construct.

If the test fails to include parts of the construct, or irrelevant parts are included, the validity of the instrument is threatened, which brings your results into question.

Construct validity refers to how well a test measures the concept (or construct) it was designed to measure. Assessing construct validity is especially important when you’re researching concepts that can’t be quantified and/or are intangible, like introversion. To ensure construct validity your test should be based on known indicators of introversion ( operationalisation ).

On the other hand, content validity assesses how well the test represents all aspects of the construct. If some aspects are missing or irrelevant parts are included, the test has low content validity.

• Discriminant validity indicates whether two tests that should not be highly related to each other are indeed not related

Construct validity has convergent and discriminant subtypes. They assist determine if a test measures the intended notion.

The reproducibility and replicability of a study can be ensured by writing a transparent, detailed method section and using clear, unambiguous language.

Reproducibility and replicability are related terms.

• A successful reproduction shows that the data analyses were conducted in a fair and honest manner.
• A successful replication shows that the reliability of the results is high.
• Reproducing research entails reanalysing the existing data in the same manner.
• Replicating (or repeating ) the research entails reconducting the entire analysis, including the collection of new data . 

Snowball sampling is a non-probability sampling method . Unlike probability sampling (which involves some form of random selection ), the initial individuals selected to be studied are the ones who recruit new participants.

Because not every member of the target population has an equal chance of being recruited into the sample, selection in snowball sampling is non-random.

Snowball sampling is a non-probability sampling method , where there is not an equal chance for every member of the population to be included in the sample .

This means that you cannot use inferential statistics and make generalisations – often the goal of quantitative research . As such, a snowball sample is not representative of the target population, and is usually a better fit for qualitative research .

Snowball sampling relies on the use of referrals. Here, the researcher recruits one or more initial participants, who then recruit the next ones. 

Participants share similar characteristics and/or know each other. Because of this, not every member of the population has an equal chance of being included in the sample, giving rise to sampling bias .

Snowball sampling is best used in the following cases:

• If there is no sampling frame available (e.g., people with a rare disease)
• If the population of interest is hard to access or locate (e.g., people experiencing homelessness)
• If the research focuses on a sensitive topic (e.g., extra-marital affairs)

Stratified sampling and quota sampling both involve dividing the population into subgroups and selecting units from each subgroup. The purpose in both cases is to select a representative sample and/or to allow comparisons between subgroups.

The main difference is that in stratified sampling, you draw a random sample from each subgroup ( probability sampling ). In quota sampling you select a predetermined number or proportion of units, in a non-random manner ( non-probability sampling ).

Random sampling or probability sampling is based on random selection. This means that each unit has an equal chance (i.e., equal probability) of being included in the sample.

On the other hand, convenience sampling involves stopping people at random, which means that not everyone has an equal chance of being selected depending on the place, time, or day you are collecting your data.

Convenience sampling and quota sampling are both non-probability sampling methods. They both use non-random criteria like availability, geographical proximity, or expert knowledge to recruit study participants.

However, in convenience sampling, you continue to sample units or cases until you reach the required sample size.

In quota sampling, you first need to divide your population of interest into subgroups (strata) and estimate their proportions (quota) in the population. Then you can start your data collection , using convenience sampling to recruit participants, until the proportions in each subgroup coincide with the estimated proportions in the population.

A sampling frame is a list of every member in the entire population . It is important that the sampling frame is as complete as possible, so that your sample accurately reflects your population.

Stratified and cluster sampling may look similar, but bear in mind that groups created in cluster sampling are heterogeneous , so the individual characteristics in the cluster vary. In contrast, groups created in stratified sampling are homogeneous , as units share characteristics.

Relatedly, in cluster sampling you randomly select entire groups and include all units of each group in your sample. However, in stratified sampling, you select some units of all groups and include them in your sample. In this way, both methods can ensure that your sample is representative of the target population .

When your population is large in size, geographically dispersed, or difficult to contact, it’s necessary to use a sampling method .

This allows you to gather information from a smaller part of the population, i.e. the sample, and make accurate statements by using statistical analysis. A few sampling methods include simple random sampling , convenience sampling , and snowball sampling .

The two main types of social desirability bias are:

• Self-deceptive enhancement (self-deception): The tendency to see oneself in a favorable light without realizing it.
• Impression managemen t (other-deception): The tendency to inflate one’s abilities or achievement in order to make a good impression on other people.

Response bias refers to conditions or factors that take place during the process of responding to surveys, affecting the responses. One type of response bias is social desirability bias .

Demand characteristics are aspects of experiments that may give away the research objective to participants. Social desirability bias occurs when participants automatically try to respond in ways that make them seem likeable in a study, even if it means misrepresenting how they truly feel.

Participants may use demand characteristics to infer social norms or experimenter expectancies and act in socially desirable ways, so you should try to control for demand characteristics wherever possible.

A systematic review is secondary research because it uses existing research. You don’t collect new data yourself.

Ethical considerations in research are a set of principles that guide your research designs and practices. These principles include voluntary participation, informed consent, anonymity, confidentiality, potential for harm, and results communication.

Scientists and researchers must always adhere to a certain code of conduct when collecting data from others .

These considerations protect the rights of research participants, enhance research validity , and maintain scientific integrity.

Research ethics matter for scientific integrity, human rights and dignity, and collaboration between science and society. These principles make sure that participation in studies is voluntary, informed, and safe.

Research misconduct means making up or falsifying data, manipulating data analyses, or misrepresenting results in research reports. It’s a form of academic fraud.

These actions are committed intentionally and can have serious consequences; research misconduct is not a simple mistake or a point of disagreement but a serious ethical failure.

Anonymity means you don’t know who the participants are, while confidentiality means you know who they are but remove identifying information from your research report. Both are important ethical considerations .

You can only guarantee anonymity by not collecting any personally identifying information – for example, names, phone numbers, email addresses, IP addresses, physical characteristics, photos, or videos.

You can keep data confidential by using aggregate information in your research report, so that you only refer to groups of participants rather than individuals.

Peer review is a process of evaluating submissions to an academic journal. Utilising rigorous criteria, a panel of reviewers in the same subject area decide whether to accept each submission for publication.

For this reason, academic journals are often considered among the most credible sources you can use in a research project – provided that the journal itself is trustworthy and well regarded.

In general, the peer review process follows the following steps:

• First, the author submits the manuscript to the editor.
• Reject the manuscript and send it back to author, or
• Send it onward to the selected peer reviewer(s)
• Next, the peer review process occurs. The reviewer provides feedback, addressing any major or minor issues with the manuscript, and gives their advice regarding what edits should be made.
• Lastly, the edited manuscript is sent back to the author. They input the edits, and resubmit it to the editor for publication.

Peer review can stop obviously problematic, falsified, or otherwise untrustworthy research from being published. It also represents an excellent opportunity to get feedback from renowned experts in your field.

It acts as a first defence, helping you ensure your argument is clear and that there are no gaps, vague terms, or unanswered questions for readers who weren’t involved in the research process.

Peer-reviewed articles are considered a highly credible source due to this stringent process they go through before publication.

Many academic fields use peer review , largely to determine whether a manuscript is suitable for publication. Peer review enhances the credibility of the published manuscript.

However, peer review is also common in non-academic settings. The United Nations, the European Union, and many individual nations use peer review to evaluate grant applications. It is also widely used in medical and health-related fields as a teaching or quality-of-care measure.

Peer assessment is often used in the classroom as a pedagogical tool. Both receiving feedback and providing it are thought to enhance the learning process, helping students think critically and collaboratively.

• In a single-blind study , only the participants are blinded.
• In a double-blind study , both participants and experimenters are blinded.
• In a triple-blind study , the assignment is hidden not only from participants and experimenters, but also from the researchers analysing the data.

Blinding is important to reduce bias (e.g., observer bias , demand characteristics ) and ensure a study’s internal validity .

If participants know whether they are in a control or treatment group , they may adjust their behaviour in ways that affect the outcome that researchers are trying to measure. If the people administering the treatment are aware of group assignment, they may treat participants differently and thus directly or indirectly influence the final results.

Blinding means hiding who is assigned to the treatment group and who is assigned to the control group in an experiment .

Explanatory research is a research method used to investigate how or why something occurs when only a small amount of information is available pertaining to that topic. It can help you increase your understanding of a given topic.

Explanatory research is used to investigate how or why a phenomenon occurs. Therefore, this type of research is often one of the first stages in the research process , serving as a jumping-off point for future research.

Exploratory research is a methodology approach that explores research questions that have not previously been studied in depth. It is often used when the issue you’re studying is new, or the data collection process is challenging in some way.

Exploratory research is often used when the issue you’re studying is new or when the data collection process is challenging for some reason.

You can use exploratory research if you have a general idea or a specific question that you want to study but there is no preexisting knowledge or paradigm with which to study it.

To implement random assignment , assign a unique number to every member of your study’s sample .

Then, you can use a random number generator or a lottery method to randomly assign each number to a control or experimental group. You can also do so manually, by flipping a coin or rolling a die to randomly assign participants to groups.

Random selection, or random sampling , is a way of selecting members of a population for your study’s sample.

In contrast, random assignment is a way of sorting the sample into control and experimental groups.

Random sampling enhances the external validity or generalisability of your results, while random assignment improves the internal validity of your study.

Random assignment is used in experiments with a between-groups or independent measures design. In this research design, there’s usually a control group and one or more experimental groups. Random assignment helps ensure that the groups are comparable.

In general, you should always use random assignment in this type of experimental design when it is ethically possible and makes sense for your study topic.

Clean data are valid, accurate, complete, consistent, unique, and uniform. Dirty data include inconsistencies and errors.

Dirty data can come from any part of the research process, including poor research design , inappropriate measurement materials, or flawed data entry.

Data cleaning takes place between data collection and data analyses. But you can use some methods even before collecting data.

For clean data, you should start by designing measures that collect valid data. Data validation at the time of data entry or collection helps you minimize the amount of data cleaning you’ll need to do.

After data collection, you can use data standardisation and data transformation to clean your data. You’ll also deal with any missing values, outliers, and duplicate values.

Data cleaning involves spotting and resolving potential data inconsistencies or errors to improve your data quality. An error is any value (e.g., recorded weight) that doesn’t reflect the true value (e.g., actual weight) of something that’s being measured.

In this process, you review, analyse, detect, modify, or remove ‘dirty’ data to make your dataset ‘clean’. Data cleaning is also called data cleansing or data scrubbing.

Data cleaning is necessary for valid and appropriate analyses. Dirty data contain inconsistencies or errors , but cleaning your data helps you minimise or resolve these.

Without data cleaning, you could end up with a Type I or II error in your conclusion. These types of erroneous conclusions can be practically significant with important consequences, because they lead to misplaced investments or missed opportunities.

Observer bias occurs when a researcher’s expectations, opinions, or prejudices influence what they perceive or record in a study. It usually affects studies when observers are aware of the research aims or hypotheses. This type of research bias is also called detection bias or ascertainment bias .

The observer-expectancy effect occurs when researchers influence the results of their own study through interactions with participants.

Researchers’ own beliefs and expectations about the study results may unintentionally influence participants through demand characteristics .

You can use several tactics to minimise observer bias .

• Use masking (blinding) to hide the purpose of your study from all observers.
• Triangulate your data with different data collection methods or sources.
• Use multiple observers and ensure inter-rater reliability.
• Train your observers to make sure data is consistently recorded between them.
• Standardise your observation procedures to make sure they are structured and clear.

Naturalistic observation is a valuable tool because of its flexibility, external validity , and suitability for topics that can’t be studied in a lab setting.

The downsides of naturalistic observation include its lack of scientific control , ethical considerations , and potential for bias from observers and subjects.

Naturalistic observation is a qualitative research method where you record the behaviours of your research subjects in real-world settings. You avoid interfering or influencing anything in a naturalistic observation.

You can think of naturalistic observation as ‘people watching’ with a purpose.

Closed-ended, or restricted-choice, questions offer respondents a fixed set of choices to select from. These questions are easier to answer quickly.

Open-ended or long-form questions allow respondents to answer in their own words. Because there are no restrictions on their choices, respondents can answer in ways that researchers may not have otherwise considered.

You can organise the questions logically, with a clear progression from simple to complex, or randomly between respondents. A logical flow helps respondents process the questionnaire easier and quicker, but it may lead to bias. Randomisation can minimise the bias from order effects.

Questionnaires can be self-administered or researcher-administered.

Self-administered questionnaires can be delivered online or in paper-and-pen formats, in person or by post. All questions are standardised so that all respondents receive the same questions with identical wording.

Researcher-administered questionnaires are interviews that take place by phone, in person, or online between researchers and respondents. You can gain deeper insights by clarifying questions for respondents or asking follow-up questions.

In a controlled experiment , all extraneous variables are held constant so that they can’t influence the results. Controlled experiments require:

• A control group that receives a standard treatment, a fake treatment, or no treatment
• Random assignment of participants to ensure the groups are equivalent

Depending on your study topic, there are various other methods of controlling variables .

An experimental group, also known as a treatment group, receives the treatment whose effect researchers wish to study, whereas a control group does not. They should be identical in all other ways.

A true experiment (aka a controlled experiment) always includes at least one control group that doesn’t receive the experimental treatment.

However, some experiments use a within-subjects design to test treatments without a control group. In these designs, you usually compare one group’s outcomes before and after a treatment (instead of comparing outcomes between different groups).

For strong internal validity , it’s usually best to include a control group if possible. Without a control group, it’s harder to be certain that the outcome was caused by the experimental treatment and not by other variables.

A questionnaire is a data collection tool or instrument, while a survey is an overarching research method that involves collecting and analysing data from people using questionnaires.

A Likert scale is a rating scale that quantitatively assesses opinions, attitudes, or behaviours. It is made up of four or more questions that measure a single attitude or trait when response scores are combined.

To use a Likert scale in a survey , you present participants with Likert-type questions or statements, and a continuum of items, usually with five or seven possible responses, to capture their degree of agreement.

Individual Likert-type questions are generally considered ordinal data , because the items have clear rank order, but don’t have an even distribution.

Overall Likert scale scores are sometimes treated as interval data. These scores are considered to have directionality and even spacing between them.

The type of data determines what statistical tests you should use to analyse your data.

A research hypothesis is your proposed answer to your research question. The research hypothesis usually includes an explanation (‘ x affects y because …’).

A statistical hypothesis, on the other hand, is a mathematical statement about a population parameter. Statistical hypotheses always come in pairs: the null and alternative hypotheses. In a well-designed study , the statistical hypotheses correspond logically to the research hypothesis.

Cross-sectional studies are less expensive and time-consuming than many other types of study. They can provide useful insights into a population’s characteristics and identify correlations for further research.

Sometimes only cross-sectional data are available for analysis; other times your research question may only require a cross-sectional study to answer it.

Cross-sectional studies cannot establish a cause-and-effect relationship or analyse behaviour over a period of time. To investigate cause and effect, you need to do a longitudinal study or an experimental study .

Longitudinal studies and cross-sectional studies are two different types of research design . In a cross-sectional study you collect data from a population at a specific point in time; in a longitudinal study you repeatedly collect data from the same sample over an extended period of time.

Longitudinal studies are better to establish the correct sequence of events, identify changes over time, and provide insight into cause-and-effect relationships, but they also tend to be more expensive and time-consuming than other types of studies.

The 1970 British Cohort Study , which has collected data on the lives of 17,000 Brits since their births in 1970, is one well-known example of a longitudinal study .

Longitudinal studies can last anywhere from weeks to decades, although they tend to be at least a year long.

A correlation reflects the strength and/or direction of the association between two or more variables.

• A positive correlation means that both variables change in the same direction.
• A negative correlation means that the variables change in opposite directions.
• A zero correlation means there’s no relationship between the variables.

A correlational research design investigates relationships between two variables (or more) without the researcher controlling or manipulating any of them. It’s a non-experimental type of quantitative research .

A correlation coefficient is a single number that describes the strength and direction of the relationship between your variables.

Different types of correlation coefficients might be appropriate for your data based on their levels of measurement and distributions . The Pearson product-moment correlation coefficient (Pearson’s r ) is commonly used to assess a linear relationship between two quantitative variables.

Controlled experiments establish causality, whereas correlational studies only show associations between variables.

• In an experimental design , you manipulate an independent variable and measure its effect on a dependent variable. Other variables are controlled so they can’t impact the results.
• In a correlational design , you measure variables without manipulating any of them. You can test whether your variables change together, but you can’t be sure that one variable caused a change in another.

In general, correlational research is high in external validity while experimental research is high in internal validity .

The third variable and directionality problems are two main reasons why correlation isn’t causation .

The third variable problem means that a confounding variable affects both variables to make them seem causally related when they are not.

The directionality problem is when two variables correlate and might actually have a causal relationship, but it’s impossible to conclude which variable causes changes in the other.

As a rule of thumb, questions related to thoughts, beliefs, and feelings work well in focus groups . Take your time formulating strong questions, paying special attention to phrasing. Be careful to avoid leading questions , which can bias your responses.

Overall, your focus group questions should be:

• Open-ended and flexible
• Impossible to answer with ‘yes’ or ‘no’ (questions that start with ‘why’ or ‘how’ are often best)
• Unambiguous, getting straight to the point while still stimulating discussion
• Unbiased and neutral

Social desirability bias is the tendency for interview participants to give responses that will be viewed favourably by the interviewer or other participants. It occurs in all types of interviews and surveys , but is most common in semi-structured interviews , unstructured interviews , and focus groups .

Social desirability bias can be mitigated by ensuring participants feel at ease and comfortable sharing their views. Make sure to pay attention to your own body language and any physical or verbal cues, such as nodding or widening your eyes.

This type of bias in research can also occur in observations if the participants know they’re being observed. They might alter their behaviour accordingly.

A focus group is a research method that brings together a small group of people to answer questions in a moderated setting. The group is chosen due to predefined demographic traits, and the questions are designed to shed light on a topic of interest. It is one of four types of interviews .

The four most common types of interviews are:

• Structured interviews : The questions are predetermined in both topic and order.
• Semi-structured interviews : A few questions are predetermined, but other questions aren’t planned.
• Unstructured interviews : None of the questions are predetermined.
• Focus group interviews : The questions are presented to a group instead of one individual.

An unstructured interview is the most flexible type of interview, but it is not always the best fit for your research topic.

Unstructured interviews are best used when:

• You are an experienced interviewer and have a very strong background in your research topic, since it is challenging to ask spontaneous, colloquial questions
• Your research question is exploratory in nature. While you may have developed hypotheses, you are open to discovering new or shifting viewpoints through the interview process.
• You are seeking descriptive data, and are ready to ask questions that will deepen and contextualise your initial thoughts and hypotheses
• Your research depends on forming connections with your participants and making them feel comfortable revealing deeper emotions, lived experiences, or thoughts

A semi-structured interview is a blend of structured and unstructured types of interviews. Semi-structured interviews are best used when:

• You have prior interview experience. Spontaneous questions are deceptively challenging, and it’s easy to accidentally ask a leading question or make a participant uncomfortable.
• Your research question is exploratory in nature. Participant answers can guide future research questions and help you develop a more robust knowledge base for future research.

The interviewer effect is a type of bias that emerges when a characteristic of an interviewer (race, age, gender identity, etc.) influences the responses given by the interviewee.

There is a risk of an interviewer effect in all types of interviews , but it can be mitigated by writing really high-quality interview questions.

A structured interview is a data collection method that relies on asking questions in a set order to collect data on a topic. They are often quantitative in nature. Structured interviews are best used when:

• You already have a very clear understanding of your topic. Perhaps significant research has already been conducted, or you have done some prior research yourself, but you already possess a baseline for designing strong structured questions.
• You are constrained in terms of time or resources and need to analyse your data quickly and efficiently
• Your research question depends on strong parity between participants, with environmental conditions held constant

More flexible interview options include semi-structured interviews , unstructured interviews , and focus groups .

When conducting research, collecting original data has significant advantages:

• You can tailor data collection to your specific research aims (e.g., understanding the needs of your consumers or user testing your website).
• You can control and standardise the process for high reliability and validity (e.g., choosing appropriate measurements and sampling methods ).

However, there are also some drawbacks: data collection can be time-consuming, labour-intensive, and expensive. In some cases, it’s more efficient to use secondary data that has already been collected by someone else, but the data might be less reliable.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organisations.

A mediator variable explains the process through which two variables are related, while a moderator variable affects the strength and direction of that relationship.

A confounder is a third variable that affects variables of interest and makes them seem related when they are not. In contrast, a mediator is the mechanism of a relationship between two variables: it explains the process by which they are related.

If something is a mediating variable :

• It’s caused by the independent variable
• It influences the dependent variable
• When it’s taken into account, the statistical correlation between the independent and dependent variables is higher than when it isn’t considered

Including mediators and moderators in your research helps you go beyond studying a simple relationship between two variables for a fuller picture of the real world. They are important to consider when studying complex correlational or causal relationships.

Mediators are part of the causal pathway of an effect, and they tell you how or why an effect takes place. Moderators usually help you judge the external validity of your study by identifying the limitations of when the relationship between variables holds.

You can think of independent and dependent variables in terms of cause and effect: an independent variable is the variable you think is the cause , while a dependent variable is the effect .

In an experiment, you manipulate the independent variable and measure the outcome in the dependent variable. For example, in an experiment about the effect of nutrients on crop growth:

• The  independent variable  is the amount of nutrients added to the crop field.
• The  dependent variable is the biomass of the crops at harvest time.

Defining your variables, and deciding how you will manipulate and measure them, is an important part of experimental design .

Discrete and continuous variables are two types of quantitative variables :

• Discrete variables represent counts (e.g., the number of objects in a collection).
• Continuous variables represent measurable amounts (e.g., water volume or weight).

Quantitative variables are any variables where the data represent amounts (e.g. height, weight, or age).

Categorical variables are any variables where the data represent groups. This includes rankings (e.g. finishing places in a race), classifications (e.g. brands of cereal), and binary outcomes (e.g. coin flips).

You need to know what type of variables you are working with to choose the right statistical test for your data and interpret your results .

Determining cause and effect is one of the most important parts of scientific research. It’s essential to know which is the cause – the independent variable – and which is the effect – the dependent variable.

You want to find out how blood sugar levels are affected by drinking diet cola and regular cola, so you conduct an experiment .

• The type of cola – diet or regular – is the independent variable .
• The level of blood sugar that you measure is the dependent variable – it changes depending on the type of cola.

No. The value of a dependent variable depends on an independent variable, so a variable cannot be both independent and dependent at the same time. It must be either the cause or the effect, not both.

Yes, but including more than one of either type requires multiple research questions .

For example, if you are interested in the effect of a diet on health, you can use multiple measures of health: blood sugar, blood pressure, weight, pulse, and many more. Each of these is its own dependent variable with its own research question.

You could also choose to look at the effect of exercise levels as well as diet, or even the additional effect of the two combined. Each of these is a separate independent variable .

To ensure the internal validity of an experiment , you should only change one independent variable at a time.

To ensure the internal validity of your research, you must consider the impact of confounding variables. If you fail to account for them, you might over- or underestimate the causal relationship between your independent and dependent variables , or even find a causal relationship where none exists.

A confounding variable is closely related to both the independent and dependent variables in a study. An independent variable represents the supposed cause , while the dependent variable is the supposed effect . A confounding variable is a third variable that influences both the independent and dependent variables.

Failing to account for confounding variables can cause you to wrongly estimate the relationship between your independent and dependent variables.

There are several methods you can use to decrease the impact of confounding variables on your research: restriction, matching, statistical control, and randomisation.

In restriction , you restrict your sample by only including certain subjects that have the same values of potential confounding variables.

In matching , you match each of the subjects in your treatment group with a counterpart in the comparison group. The matched subjects have the same values on any potential confounding variables, and only differ in the independent variable .

In statistical control , you include potential confounders as variables in your regression .

In randomisation , you randomly assign the treatment (or independent variable) in your study to a sufficiently large number of subjects, which allows you to control for all potential confounding variables.

In scientific research, concepts are the abstract ideas or phenomena that are being studied (e.g., educational achievement). Variables are properties or characteristics of the concept (e.g., performance at school), while indicators are ways of measuring or quantifying variables (e.g., yearly grade reports).

The process of turning abstract concepts into measurable variables and indicators is called operationalisation .

In statistics, ordinal and nominal variables are both considered categorical variables .

Even though ordinal data can sometimes be numerical, not all mathematical operations can be performed on them.

A control variable is any variable that’s held constant in a research study. It’s not a variable of interest in the study, but it’s controlled because it could influence the outcomes.

Control variables help you establish a correlational or causal relationship between variables by enhancing internal validity .

If you don’t control relevant extraneous variables , they may influence the outcomes of your study, and you may not be able to demonstrate that your results are really an effect of your independent variable .

‘Controlling for a variable’ means measuring extraneous variables and accounting for them statistically to remove their effects on other variables.

Researchers often model control variable data along with independent and dependent variable data in regression analyses and ANCOVAs . That way, you can isolate the control variable’s effects from the relationship between the variables of interest.

An extraneous variable is any variable that you’re not investigating that can potentially affect the dependent variable of your research study.

A confounding variable is a type of extraneous variable that not only affects the dependent variable, but is also related to the independent variable.

There are 4 main types of extraneous variables :

• Demand characteristics : Environmental cues that encourage participants to conform to researchers’ expectations
• Experimenter effects : Unintentional actions by researchers that influence study outcomes
• Situational variables : Eenvironmental variables that alter participants’ behaviours
• Participant variables : Any characteristic or aspect of a participant’s background that could affect study results

The difference between explanatory and response variables is simple:

• An explanatory variable is the expected cause, and it explains the results.
• A response variable is the expected effect, and it responds to other variables.

The term ‘ explanatory variable ‘ is sometimes preferred over ‘ independent variable ‘ because, in real-world contexts, independent variables are often influenced by other variables. This means they aren’t totally independent.

Multiple independent variables may also be correlated with each other, so ‘explanatory variables’ is a more appropriate term.

On graphs, the explanatory variable is conventionally placed on the x -axis, while the response variable is placed on the y -axis.

• If you have quantitative variables , use a scatterplot or a line graph.
• If your response variable is categorical, use a scatterplot or a line graph.
• If your explanatory variable is categorical, use a bar graph.

A correlation is usually tested for two variables at a time, but you can test correlations between three or more variables.

An independent variable is the variable you manipulate, control, or vary in an experimental study to explore its effects. It’s called ‘independent’ because it’s not influenced by any other variables in the study.

Independent variables are also called:

• Explanatory variables (they explain an event or outcome)
• Predictor variables (they can be used to predict the value of a dependent variable)
• Right-hand-side variables (they appear on the right-hand side of a regression equation)

A dependent variable is what changes as a result of the independent variable manipulation in experiments . It’s what you’re interested in measuring, and it ‘depends’ on your independent variable.

In statistics, dependent variables are also called:

• Response variables (they respond to a change in another variable)
• Outcome variables (they represent the outcome you want to measure)
• Left-hand-side variables (they appear on the left-hand side of a regression equation)

Deductive reasoning is commonly used in scientific research, and it’s especially associated with quantitative research .

In research, you might have come across something called the hypothetico-deductive method . It’s the scientific method of testing hypotheses to check whether your predictions are substantiated by real-world data.

Deductive reasoning is a logical approach where you progress from general ideas to specific conclusions. It’s often contrasted with inductive reasoning , where you start with specific observations and form general conclusions.

Deductive reasoning is also called deductive logic.

Inductive reasoning is a method of drawing conclusions by going from the specific to the general. It’s usually contrasted with deductive reasoning, where you proceed from general information to specific conclusions.

Inductive reasoning is also called inductive logic or bottom-up reasoning.

In inductive research , you start by making observations or gathering data. Then, you take a broad scan of your data and search for patterns. Finally, you make general conclusions that you might incorporate into theories.

Inductive reasoning is a bottom-up approach, while deductive reasoning is top-down.

Inductive reasoning takes you from the specific to the general, while in deductive reasoning, you make inferences by going from general premises to specific conclusions.

There are many different types of inductive reasoning that people use formally or informally.

Here are a few common types:

• Inductive generalisation : You use observations about a sample to come to a conclusion about the population it came from.
• Statistical generalisation: You use specific numbers about samples to make statements about populations.
• Causal reasoning: You make cause-and-effect links between different things.
• Sign reasoning: You make a conclusion about a correlational relationship between different things.
• Analogical reasoning: You make a conclusion about something based on its similarities to something else.

It’s often best to ask a variety of people to review your measurements. You can ask experts, such as other researchers, or laypeople, such as potential participants, to judge the face validity of tests.

While experts have a deep understanding of research methods , the people you’re studying can provide you with valuable insights you may have missed otherwise.

Face validity is important because it’s a simple first step to measuring the overall validity of a test or technique. It’s a relatively intuitive, quick, and easy way to start checking whether a new measure seems useful at first glance.

Good face validity means that anyone who reviews your measure says that it seems to be measuring what it’s supposed to. With poor face validity, someone reviewing your measure may be left confused about what you’re measuring and why you’re using this method.

Face validity is about whether a test appears to measure what it’s supposed to measure. This type of validity is concerned with whether a measure seems relevant and appropriate for what it’s assessing only on the surface.

Statistical analyses are often applied to test validity with data from your measures. You test convergent validity and discriminant validity with correlations to see if results from your test are positively or negatively related to those of other established tests.

You can also use regression analyses to assess whether your measure is actually predictive of outcomes that you expect it to predict theoretically. A regression analysis that supports your expectations strengthens your claim of construct validity .

When designing or evaluating a measure, construct validity helps you ensure you’re actually measuring the construct you’re interested in. If you don’t have construct validity, you may inadvertently measure unrelated or distinct constructs and lose precision in your research.

Construct validity is often considered the overarching type of measurement validity ,  because it covers all of the other types. You need to have face validity , content validity, and criterion validity to achieve construct validity.

Construct validity is about how well a test measures the concept it was designed to evaluate. It’s one of four types of measurement validity , which includes construct validity, face validity , and criterion validity.

There are two subtypes of construct validity.

• Convergent validity : The extent to which your measure corresponds to measures of related constructs
• Discriminant validity: The extent to which your measure is unrelated or negatively related to measures of distinct constructs

Attrition bias can skew your sample so that your final sample differs significantly from your original sample. Your sample is biased because some groups from your population are underrepresented.

With a biased final sample, you may not be able to generalise your findings to the original population that you sampled from, so your external validity is compromised.

There are seven threats to external validity : selection bias , history, experimenter effect, Hawthorne effect , testing effect, aptitude-treatment, and situation effect.

The two types of external validity are population validity (whether you can generalise to other groups of people) and ecological validity (whether you can generalise to other situations and settings).

The external validity of a study is the extent to which you can generalise your findings to different groups of people, situations, and measures.

Attrition bias is a threat to internal validity . In experiments, differential rates of attrition between treatment and control groups can skew results.

This bias can affect the relationship between your independent and dependent variables . It can make variables appear to be correlated when they are not, or vice versa.

Internal validity is the extent to which you can be confident that a cause-and-effect relationship established in a study cannot be explained by other factors.

There are eight threats to internal validity : history, maturation, instrumentation, testing, selection bias , regression to the mean, social interaction, and attrition .

A sampling error is the difference between a population parameter and a sample statistic .

A statistic refers to measures about the sample , while a parameter refers to measures about the population .

Populations are used when a research question requires data from every member of the population. This is usually only feasible when the population is small and easily accessible.

Systematic sampling is a probability sampling method where researchers select members of the population at a regular interval – for example, by selecting every 15th person on a list of the population. If the population is in a random order, this can imitate the benefits of simple random sampling .

There are three key steps in systematic sampling :

• Define and list your population , ensuring that it is not ordered in a cyclical or periodic order.
• Decide on your sample size and calculate your interval, k , by dividing your population by your target sample size.
• Choose every k th member of the population as your sample.

Yes, you can create a stratified sample using multiple characteristics, but you must ensure that every participant in your study belongs to one and only one subgroup. In this case, you multiply the numbers of subgroups for each characteristic to get the total number of groups.

For example, if you were stratifying by location with three subgroups (urban, rural, or suburban) and marital status with five subgroups (single, divorced, widowed, married, or partnered), you would have 3 × 5 = 15 subgroups.

You should use stratified sampling when your sample can be divided into mutually exclusive and exhaustive subgroups that you believe will take on different mean values for the variable that you’re studying.

Using stratified sampling will allow you to obtain more precise (with lower variance ) statistical estimates of whatever you are trying to measure.

For example, say you want to investigate how income differs based on educational attainment, but you know that this relationship can vary based on race. Using stratified sampling, you can ensure you obtain a large enough sample from each racial group, allowing you to draw more precise conclusions.

In stratified sampling , researchers divide subjects into subgroups called strata based on characteristics that they share (e.g., race, gender, educational attainment).

Once divided, each subgroup is randomly sampled using another probability sampling method .

Multistage sampling can simplify data collection when you have large, geographically spread samples, and you can obtain a probability sample without a complete sampling frame.

But multistage sampling may not lead to a representative sample, and larger samples are needed for multistage samples to achieve the statistical properties of simple random samples .

In multistage sampling , you can use probability or non-probability sampling methods.

For a probability sample, you have to probability sampling at every stage. You can mix it up by using simple random sampling , systematic sampling , or stratified sampling to select units at different stages, depending on what is applicable and relevant to your study.

Cluster sampling is a probability sampling method in which you divide a population into clusters, such as districts or schools, and then randomly select some of these clusters as your sample.

The clusters should ideally each be mini-representations of the population as a whole.

There are three types of cluster sampling : single-stage, double-stage and multi-stage clustering. In all three types, you first divide the population into clusters, then randomly select clusters for use in your sample.

• In single-stage sampling , you collect data from every unit within the selected clusters.
• In double-stage sampling , you select a random sample of units from within the clusters.
• In multi-stage sampling , you repeat the procedure of randomly sampling elements from within the clusters until you have reached a manageable sample.

Cluster sampling is more time- and cost-efficient than other probability sampling methods , particularly when it comes to large samples spread across a wide geographical area.

However, it provides less statistical certainty than other methods, such as simple random sampling , because it is difficult to ensure that your clusters properly represent the population as a whole.

If properly implemented, simple random sampling is usually the best sampling method for ensuring both internal and external validity . However, it can sometimes be impractical and expensive to implement, depending on the size of the population to be studied,

If you have a list of every member of the population and the ability to reach whichever members are selected, you can use simple random sampling.

The American Community Survey  is an example of simple random sampling . In order to collect detailed data on the population of the US, the Census Bureau officials randomly select 3.5 million households per year and use a variety of methods to convince them to fill out the survey.

Simple random sampling is a type of probability sampling in which the researcher randomly selects a subset of participants from a population . Each member of the population has an equal chance of being selected. Data are then collected from as large a percentage as possible of this random subset.

Sampling bias occurs when some members of a population are systematically more likely to be selected in a sample than others.

In multistage sampling , or multistage cluster sampling, you draw a sample from a population using smaller and smaller groups at each stage.

This method is often used to collect data from a large, geographically spread group of people in national surveys, for example. You take advantage of hierarchical groupings (e.g., from county to city to neighbourhood) to create a sample that’s less expensive and time-consuming to collect data from.

In non-probability sampling , the sample is selected based on non-random criteria, and not every member of the population has a chance of being included.

Common non-probability sampling methods include convenience sampling , voluntary response sampling, purposive sampling , snowball sampling , and quota sampling .

Probability sampling means that every member of the target population has a known chance of being included in the sample.

Probability sampling methods include simple random sampling , systematic sampling , stratified sampling , and cluster sampling .

Samples are used to make inferences about populations . Samples are easier to collect data from because they are practical, cost-effective, convenient, and manageable.

While a between-subjects design has fewer threats to internal validity , it also requires more participants for high statistical power than a within-subjects design .

Advantages:

• Prevents carryover effects of learning and fatigue.
• Shorter study duration.

Disadvantages:

• Needs larger samples for high power.
• Uses more resources to recruit participants, administer sessions, cover costs, etc.
• Individual differences may be an alternative explanation for results.

In a factorial design, multiple independent variables are tested.

If you test two variables, each level of one independent variable is combined with each level of the other independent variable to create different conditions.

Yes. Between-subjects and within-subjects designs can be combined in a single study when you have two or more independent variables (a factorial design). In a mixed factorial design, one variable is altered between subjects and another is altered within subjects.

Within-subjects designs have many potential threats to internal validity , but they are also very statistically powerful .

• Only requires small samples
• Statistically powerful
• Removes the effects of individual differences on the outcomes
• Internal validity threats reduce the likelihood of establishing a direct relationship between variables
• Time-related effects, such as growth, can influence the outcomes
• Carryover effects mean that the specific order of different treatments affect the outcomes

Quasi-experimental design is most useful in situations where it would be unethical or impractical to run a true experiment .

Quasi-experiments have lower internal validity than true experiments, but they often have higher external validity  as they can use real-world interventions instead of artificial laboratory settings.

In experimental research, random assignment is a way of placing participants from your sample into different groups using randomisation. With this method, every member of the sample has a known or equal chance of being placed in a control group or an experimental group.

A quasi-experiment is a type of research design that attempts to establish a cause-and-effect relationship. The main difference between this and a true experiment is that the groups are not randomly assigned.

In a between-subjects design , every participant experiences only one condition, and researchers assess group differences between participants in various conditions.

In a within-subjects design , each participant experiences all conditions, and researchers test the same participants repeatedly for differences between conditions.

The word ‘between’ means that you’re comparing different conditions between groups, while the word ‘within’ means you’re comparing different conditions within the same group.

A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.

A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.

In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.

Triangulation can help:

• Reduce bias that comes from using a single method, theory, or investigator
• Enhance validity by approaching the same topic with different tools
• Establish credibility by giving you a complete picture of the research problem

But triangulation can also pose problems:

• It’s time-consuming and labour-intensive, often involving an interdisciplinary team.
• Your results may be inconsistent or even contradictory.

There are four main types of triangulation :

• Data triangulation : Using data from different times, spaces, and people
• Investigator triangulation : Involving multiple researchers in collecting or analysing data
• Theory triangulation : Using varying theoretical perspectives in your research
• Methodological triangulation : Using different methodologies to approach the same topic

Experimental designs are a set of procedures that you plan in order to examine the relationship between variables that interest you.

To design a successful experiment, first identify:

• A testable hypothesis
• One or more independent variables that you will manipulate
• One or more dependent variables that you will measure

When designing the experiment, first decide:

• How your variable(s) will be manipulated
• How you will control for any potential confounding or lurking variables
• How many subjects you will include
• How you will assign treatments to your subjects

Exploratory research explores the main aspects of a new or barely researched question.

Explanatory research explains the causes and effects of an already widely researched question.

The key difference between observational studies and experiments is that, done correctly, an observational study will never influence the responses or behaviours of participants. Experimental designs will have a treatment condition applied to at least a portion of participants.

An observational study could be a good fit for your research if your research question is based on things you observe. If you have ethical, logistical, or practical concerns that make an experimental design challenging, consider an observational study. Remember that in an observational study, it is critical that there be no interference or manipulation of the research subjects. Since it’s not an experiment, there are no control or treatment groups either.

These are four of the most common mixed methods designs :

• Convergent parallel: Quantitative and qualitative data are collected at the same time and analysed separately. After both analyses are complete, compare your results to draw overall conclusions. 
• Embedded: Quantitative and qualitative data are collected at the same time, but within a larger quantitative or qualitative design. One type of data is secondary to the other.
• Explanatory sequential: Quantitative data is collected and analysed first, followed by qualitative data. You can use this design if you think your qualitative data will explain and contextualise your quantitative findings.
• Exploratory sequential: Qualitative data is collected and analysed first, followed by quantitative data. You can use this design if you think the quantitative data will confirm or validate your qualitative findings.

Triangulation in research means using multiple datasets, methods, theories and/or investigators to address a research question. It’s a research strategy that can help you enhance the validity and credibility of your findings.

Triangulation is mainly used in qualitative research , but it’s also commonly applied in quantitative research . Mixed methods research always uses triangulation.

Operationalisation means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioural avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalise the variables that you want to measure.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

There are five common approaches to qualitative research :

• Grounded theory involves collecting data in order to develop new theories.
• Ethnography involves immersing yourself in a group or organisation to understand its culture.
• Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
• Phenomenological research involves investigating phenomena through people’s lived experiences.
• Action research links theory and practice in several cycles to drive innovative changes.

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organise your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyse data (e.g. experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts, and meanings, use qualitative methods .
• If you want to analyse a large amount of readily available data, use secondary data. If you want data specific to your purposes with control over how they are generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

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Hypothesis meaning in Urdu

Hypothesis sentences, hypothesis synonyms, hypothesis definitions.

1 of 3) Hypothesis : قیاس : (noun) a proposal intended to explain certain facts or observations.

2 of 3) Hypothesis , Possibility , Theory : نظریہ : (noun) a tentative insight into the natural world; a concept that is not yet verified but that if true would explain certain facts or phenomena.

3 of 3) Hypothesis , Conjecture , Guess , Speculation , Supposition , Surmisal , Surmise : قیاس : (noun) a message expressing a viewpoint grounded on insufficient or partial evidence.

Useful Words

Contrary To Fact : حقیقت کے بر عکس , A Priori : قیاس پر مبنی , Check : تصدیق , Hypothetically : فرضاً , Assumption : مفروضہ , Conjecture : قیاس آرائی , Explanatory : بیانیہ , Determinism : جبریت , Diagram : نقشہ , Counterproposal : جوابی تجویز , Affirmative Pleading : حقیقی دفاع , Plead : استدعا کرنا , Conjecturally : قیاس سے , Dupery : فریب , Comment : تشریح کرنا , Agreement : موافقت , Offer : بیچنے کے لیے پیش کرنا , Advice : مشورہ , Proposer : تجویز کرنے والا , Memo : لکھی ہوئی یادداشت , Acquiescence : رضا مندی , Pro : کسی تجویز پر دلائل , Bid : بولی دینا , Anti : خلاف , Absolute Frequency : تعدد , Contemplate : سوچنا , Approach : پاس جانا , Proffer : دینا , Advise : تجویز دینا , Sampler : پرکھنے کی جگہ , Projection : ماضی کی بنیاد پر پیشنگوئی

Useful Words Definitions

Contrary To Fact: going counter to the facts (usually as a hypothesis).

A Priori: based on hypothesis or theory rather than experiment.

Check: additional proof that something that was believed (some fact or hypothesis or theory) is correct.

Hypothetically: by hypothesis.

Assumption: a hypothesis that is taken for granted.

Conjecture: a hypothesis that has been formed by speculating or conjecturing (usually with little hard evidence).

Explanatory: serving or intended to explain or make clear.

Determinism: (philosophy) a philosophical theory holding that all events are inevitable consequences of antecedent sufficient causes; often understood as denying the possibility of free will.

Diagram: a drawing intended to explain how something works; a drawing showing the relation between the parts.

Counterproposal: a proposal offered as an alternative to an earlier proposal.

Affirmative Pleading: any defensive pleading that affirms facts rather than merely denying the facts alleged by the plaintiff.

Plead: make an allegation in an action or other legal proceeding, especially answer the previous pleading of the other party by denying facts therein stated or by alleging new facts.

Conjecturally: in a manner involving or inclined to conjecture and supposition.

Dupery: something intended to deceive; deliberate trickery intended to gain an advantage.

Comment: explain or interpret something.

Agreement: compatibility of observations.

Offer: something offered (as a proposal or bid).

Advice: a proposal for an appropriate course of action.

Proposer: someone who advances a suggestion or proposal.

Memo: a written proposal or reminder.

Acquiescence: agreement with a statement or proposal to do something.

Pro: an argument in favor of a proposal.

Bid: a formal proposal to buy at a specified price.

Anti: not in favor of (an action or proposal etc.).

Absolute Frequency: the number of observations in a given statistical category.

Contemplate: consider as a possibility.

Approach: make advances to someone, usually with a proposal or suggestion.

Proffer: a proposal offered for acceptance or rejection.

Advise: make a proposal, declare a plan for something.

Sampler: an observation station that is set up to make sample observations of something.

Projection: a prediction made by extrapolating from past observations.

Related Words

Concept : تصور , Theory : نظریہ , Historicism : رسم و رواج کا تاریخ کے زیر اثر ہونے کا نظریہ , Proposal : تجویز

Hypothesis in Book Titles

Science and Hypothesis. The Hygiene Hypothesis and Darwinian Medicine. The Ancestress Hypothesis: Visual Art as Adaptation. Hypothesis and Evidence in Psychoanalysis and Speculation.

Next of Hypothesis

Hypothesise : to believe especially on uncertain or tentative grounds.

Previous of Hypothesis

Hyposmia : lessened sensitivity to odors.

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It helps you understand the word Hypothesis with comprehensive detail, no other web page in our knowledge can explain Hypothesis better than this page. The page not only provides Urdu meaning of Hypothesis but also gives extensive definition in English language. The definition of Hypothesis is followed by practically usable example sentences which allow you to construct your own sentences based on it. You can also find multiple synonyms or similar words of Hypothesis. All of this may seem less if you are unable to learn exact pronunciation of Hypothesis, so we have embedded mp3 recording of native Englishman, simply click on speaker icon and listen how English speaking people pronounce Hypothesis. We hope this page has helped you understand Hypothesis in detail, if you find any mistake on this page, please keep in mind that no human being can be perfect.

What is Hypothesis? Definition, Meaning, Characteristics, Sources

• Post last modified: 10 January 2022
• Reading time: 18 mins read
• Post category: Research Methodology

• What is Hypothesis?

Hypothesis is a prediction of the outcome of a study. Hypotheses are drawn from theories and research questions or from direct observations. In fact, a research problem can be formulated as a hypothesis. To test the hypothesis we need to formulate it in terms that can actually be analysed with statistical tools.

As an example, if we want to explore whether using a specific teaching method at school will result in better school marks (research question), the hypothesis could be that the mean school marks of students being taught with that specific teaching method will be higher than of those being taught using other methods.

In this example, we stated a hypothesis about the expected differences between groups. Other hypotheses may refer to correlations between variables.

Table of Content

• 1 What is Hypothesis?
• 2 Hypothesis Definition
• 3 Meaning of Hypothesis
• 4.1 Conceptual Clarity
• 4.2 Need of empirical referents
• 4.3 Hypothesis should be specific
• 4.4 Hypothesis should be within the ambit of the available research techniques
• 4.5 Hypothesis should be consistent with the theory
• 4.6 Hypothesis should be concerned with observable facts and empirical events
• 4.7 Hypothesis should be simple
• 5.1 Observation
• 5.2 Analogies
• 5.4 State of Knowledge
• 5.5 Culture
• 5.6 Continuity of Research
• 6.1 Null Hypothesis
• 6.2 Alternative Hypothesis

Thus, to formulate a hypothesis, we need to refer to the descriptive statistics (such as the mean final marks), and specify a set of conditions about these statistics (such as a difference between the means, or in a different example, a positive or negative correlation). The hypothesis we formulate applies to the population of interest.

The null hypothesis makes a statement that no difference exists (see Pyrczak, 1995, pp. 75-84).

Hypothesis Definition

A hypothesis is ‘a guess or supposition as to the existence of some fact or law which will serve to explain a connection of facts already known to exist.’ – J. E. Creighton & H. R. Smart

Hypothesis is ‘a proposition not known to be definitely true or false, examined for the sake of determining the consequences which would follow from its truth.’ – Max Black

Hypothesis is ‘a proposition which can be put to a test to determine validity and is useful for further research.’ – W. J. Goode and P. K. Hatt

A hypothesis is a proposition, condition or principle which is assumed, perhaps without belief, in order to draw out its logical consequences and by this method to test its accord with facts which are known or may be determined. – Webster’s New International Dictionary of the English Language (1956)

Meaning of Hypothesis

From the above mentioned definitions of hypothesis, its meaning can be explained in the following ways.

• At the primary level, a hypothesis is the possible and probable explanation of the sequence of happenings or data.
• Sometimes, hypothesis may emerge from an imagination, common sense or a sudden event.
• Hypothesis can be a probable answer to the research problem undertaken for study. 4. Hypothesis may not always be true. It can get disproven. In other words, hypothesis need not always be a true proposition.
• Hypothesis, in a sense, is an attempt to present the interrelations that exist in the available data or information.
• Hypothesis is not an individual opinion or community thought. Instead, it is a philosophical means which is to be used for research purpose. Hypothesis is not to be considered as the ultimate objective; rather it is to be taken as the means of explaining scientifically the prevailing situation.

The concept of hypothesis can further be explained with the help of some examples. Lord Keynes, in his theory of national income determination, made a hypothesis about the consumption function. He stated that the consumption expenditure of an individual or an economy as a whole is dependent on the level of income and changes in a certain proportion.

Later, this proposition was proved in the statistical research carried out by Prof. Simon Kuznets. Matthus, while studying the population, formulated a hypothesis that population increases faster than the supply of food grains. Population studies of several countries revealed that this hypothesis is true.

Validation of the Malthus’ hypothesis turned it into a theory and when it was tested in many other countries it became the famous Malthus’ Law of Population. It thus emerges that when a hypothesis is tested and proven, it becomes a theory. The theory, when found true in different times and at different places, becomes the law. Having understood the concept of hypothesis, few hypotheses can be formulated in the areas of commerce and economics.

• Population growth moderates with the rise in per capita income.
• Sales growth is positively linked with the availability of credit.
• Commerce education increases the employability of the graduate students.
• High rates of direct taxes prompt people to evade taxes.
• Good working conditions improve the productivity of employees.
• Advertising is the most effecting way of promoting sales than any other scheme.
• Higher Debt-Equity Ratio increases the probability of insolvency.
• Economic reforms in India have made the public sector banks more efficient and competent.
• Foreign direct investment in India has moved in those sectors which offer higher rate of profit.
• There is no significant association between credit rating and investment of fund.

Characteristics of Hypothesis

Not all the hypotheses are good and useful from the point of view of research. It is only a few hypotheses satisfying certain criteria that are good, useful and directive in the research work undertaken. The characteristics of such a useful hypothesis can be listed as below:

Conceptual Clarity

Need of empirical referents, hypothesis should be specific, hypothesis should be within the ambit of the available research techniques, hypothesis should be consistent with the theory, hypothesis should be concerned with observable facts and empirical events, hypothesis should be simple.

The concepts used while framing hypothesis should be crystal clear and unambiguous. Such concepts must be clearly defined so that they become lucid and acceptable to everyone. How are the newly developed concepts interrelated and how are they linked with the old one is to be very clear so that the hypothesis framed on their basis also carries the same clarity.

A hypothesis embodying unclear and ambiguous concepts can to a great extent undermine the successful completion of the research work.

A hypothesis can be useful in the research work undertaken only when it has links with some empirical referents. Hypothesis based on moral values and ideals are useless as they cannot be tested. Similarly, hypothesis containing opinions as good and bad or expectation with respect to something are not testable and therefore useless.

For example, ‘current account deficit can be lowered if people change their attitude towards gold’ is a hypothesis encompassing expectation. In case of such a hypothesis, the attitude towards gold is something which cannot clearly be described and therefore a hypothesis which embodies such an unclean thing cannot be tested and proved or disproved. In short, the hypothesis should be linked with some testable referents.

For the successful conduction of research, it is necessary that the hypothesis is specific and presented in a precise manner. Hypothesis which is general, too ambitious and grandiose in scope is not to be made as such hypothesis cannot be easily put to test. A hypothesis is to be based on such concepts which are precise and empirical in nature. A hypothesis should give a clear idea about the indicators which are to be used.

For example, a hypothesis that economic power is increasingly getting concentrated in a few hands in India should enable us to define the concept of economic power. It should be explicated in terms of measurable indicator like income, wealth, etc. Such specificity in the formulation of a hypothesis ensures that the research is practicable and significant.

While framing the hypothesis, the researcher should be aware of the available research techniques and should see that the hypothesis framed is testable on the basis of them. In other words, a hypothesis should be researchable and for this it is important that a due thought has been given to the methods and techniques which can be used to measure the concepts and variables embodied in the hypothesis.

It does not however mean that hypotheses which are not testable with the available techniques of research are not to be made. If the problem is too significant and therefore the hypothesis framed becomes too ambitious and complex, it’s testing becomes possible with the development of new research techniques or the hypothesis itself leads to the development of new research techniques.

A hypothesis must be related to the existing theory or should have a theoretical orientation. The growth of knowledge takes place in the sequence of facts, hypothesis, theory and law or principles. It means the hypothesis should have a correspondence with the existing facts and theory.

If the hypothesis is related to some theory, the research work will enable us to support, modify or refute the existing theory. Theoretical orientation of the hypothesis ensures that it becomes scientifically useful. According to Prof. Goode and Prof. Hatt, research work can contribute to the existing knowledge only when the hypothesis is related with some theory.

This enables us to explain the observed facts and situations and also verify the framed hypothesis. In the words of Prof. Cohen and Prof. Nagel, “hypothesis must be formulated in such a manner that deduction can be made from it and that consequently a decision can be reached as to whether it does or does not explain the facts considered.”

If the research work based on a hypothesis is to be successful, it is necessary that the later is as simple and easy as possible. An ambition of finding out something new may lead the researcher to frame an unrealistic and unclear hypothesis. Such a temptation is to be avoided. Framing a simple, easy and testable hypothesis requires that the researcher is well acquainted with the related concepts.

Sources of Hypothesis

Hypotheses can be derived from various sources. Some of the sources is given below:

Observation

State of knowledge, continuity of research.

Hypotheses can be derived from observation from the observation of price behavior in a market. For example the relationship between the price and demand for an article is hypothesized.

Analogies are another source of useful hypotheses. Julian Huxley has pointed out that casual observations in nature or in the framework of another science may be a fertile source of hypotheses. For example, the hypotheses that similar human types or activities may be found in similar geophysical regions come from plant ecology.

This is one of the main sources of hypotheses. It gives direction to research by stating what is known logical deduction from theory lead to new hypotheses. For example, profit / wealth maximization is considered as the goal of private enterprises. From this assumption various hypotheses are derived’.

An important source of hypotheses is the state of knowledge in any particular science where formal theories exist hypotheses can be deduced. If the hypotheses are rejected theories are scarce hypotheses are generated from conception frameworks.

Another source of hypotheses is the culture on which the researcher was nurtured. Western culture has induced the emergence of sociology as an academic discipline over the past decade, a large part of the hypotheses on American society examined by researchers were connected with violence. This interest is related to the considerable increase in the level of violence in America.

The continuity of research in a field itself constitutes an important source of hypotheses. The rejection of some hypotheses leads to the formulation of new ones capable of explaining dependent variables in subsequent research on the same subject.

Null and Alternative Hypothesis

Null hypothesis.

The hypothesis that are proposed with the intent of receiving a rejection for them are called Null Hypothesis . This requires that we hypothesize the opposite of what is desired to be proved. For example, if we want to show that sales and advertisement expenditure are related, we formulate the null hypothesis that they are not related.

Similarly, if we want to conclude that the new sales training programme is effective, we formulate the null hypothesis that the new training programme is not effective, and if we want to prove that the average wages of skilled workers in town 1 is greater than that of town 2, we formulate the null hypotheses that there is no difference in the average wages of the skilled workers in both the towns.

Since we hypothesize that sales and advertisement are not related, new training programme is not effective and the average wages of skilled workers in both the towns are equal, we call such hypotheses null hypotheses and denote them as H 0 .

Alternative Hypothesis

Rejection of null hypotheses leads to the acceptance of alternative hypothesis . The rejection of null hypothesis indicates that the relationship between variables (e.g., sales and advertisement expenditure) or the difference between means (e.g., wages of skilled workers in town 1 and town 2) or the difference between proportions have statistical significance and the acceptance of the null hypotheses indicates that these differences are due to chance.

As already mentioned, the alternative hypotheses specify that values/relation which the researcher believes hold true. The alternative hypotheses can cover a whole range of values rather than a single point. The alternative hypotheses are denoted by H 1 .

Business Ethics

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• Cambridge Dictionary +Plus

Meaning of hypothesis in English

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• abstraction
• afterthought
• anthropocentrism
• anti-Darwinian
• exceptionalism
• foundation stone
• great minds think alike idiom
• non-dogmatic
• non-empirical
• non-material
• non-practical
• social Darwinism
• supersensible
• the domino theory

hypothesis | Intermediate English

Hypothesis | business english, examples of hypothesis, translations of hypothesis.

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to be busy doing a lot of things but achieving very little

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• Math Article

Hypothesis Definition

In Statistics, the determination of the variation between the group of data due to true variation is done by hypothesis testing. The sample data are taken from the population parameter based on the assumptions. The hypothesis can be classified into various types. In this article, let us discuss the hypothesis definition, various types of hypothesis and the significance of hypothesis testing, which are explained in detail.

Hypothesis Definition in Statistics

In Statistics, a hypothesis is defined as a formal statement, which gives the explanation about the relationship between the two or more variables of the specified population. It helps the researcher to translate the given problem to a clear explanation for the outcome of the study. It clearly explains and predicts the expected outcome. It indicates the types of experimental design and directs the study of the research process.

Types of Hypothesis

The hypothesis can be broadly classified into different types. They are:

Simple Hypothesis

A simple hypothesis is a hypothesis that there exists a relationship between two variables. One is called a dependent variable, and the other is called an independent variable.

Complex Hypothesis

A complex hypothesis is used when there is a relationship between the existing variables. In this hypothesis, the dependent and independent variables are more than two.

Null Hypothesis

In the null hypothesis, there is no significant difference between the populations specified in the experiments, due to any experimental or sampling error. The null hypothesis is denoted by H 0 .

Alternative Hypothesis

In an alternative hypothesis, the simple observations are easily influenced by some random cause. It is denoted by the H a or H 1 .

Empirical Hypothesis

An empirical hypothesis is formed by the experiments and based on the evidence.

Statistical Hypothesis

In a statistical hypothesis, the statement should be logical or illogical, and the hypothesis is verified statistically.

Apart from these types of hypothesis, some other hypotheses are directional and non-directional hypothesis, associated hypothesis, casual hypothesis.

Characteristics of Hypothesis

The important characteristics of the hypothesis are:

• The hypothesis should be short and precise
• It should be specific
• A hypothesis must be related to the existing body of knowledge
• It should be capable of verification

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