what is p value in quantitative research

P-Value And Statistical Significance: What It Is & Why It Matters

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

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

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

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The p-value in statistics quantifies the evidence against a null hypothesis. A low p-value suggests data is inconsistent with the null, potentially favoring an alternative hypothesis. Common significance thresholds are 0.05 or 0.01.

Hypothesis testing

When you perform a statistical test, a p-value helps you determine the significance of your results in relation to the null hypothesis.

The null hypothesis (H0) states no relationship exists between the two variables being studied (one variable does not affect the other). It states the results are due to chance and are not significant in supporting the idea being investigated. Thus, the null hypothesis assumes that whatever you try to prove did not happen.

The alternative hypothesis (Ha or H1) is the one you would believe if the null hypothesis is concluded to be untrue.

The alternative hypothesis states that the independent variable affected the dependent variable, and the results are significant in supporting the theory being investigated (i.e., the results are not due to random chance).

What a p-value tells you

A p-value, or probability value, is a number describing how likely it is that your data would have occurred by random chance (i.e., that the null hypothesis is true).

The level of statistical significance is often expressed as a p-value between 0 and 1.

The smaller the p -value, the less likely the results occurred by random chance, and the stronger the evidence that you should reject the null hypothesis.

Remember, a p-value doesn’t tell you if the null hypothesis is true or false. It just tells you how likely you’d see the data you observed (or more extreme data) if the null hypothesis was true. It’s a piece of evidence, not a definitive proof.

Example: Test Statistic and p-Value

Suppose you’re conducting a study to determine whether a new drug has an effect on pain relief compared to a placebo. If the new drug has no impact, your test statistic will be close to the one predicted by the null hypothesis (no difference between the drug and placebo groups), and the resulting p-value will be close to 1. It may not be precisely 1 because real-world variations may exist. Conversely, if the new drug indeed reduces pain significantly, your test statistic will diverge further from what’s expected under the null hypothesis, and the p-value will decrease. The p-value will never reach zero because there’s always a slim possibility, though highly improbable, that the observed results occurred by random chance.

P-value interpretation

The significance level (alpha) is a set probability threshold (often 0.05), while the p-value is the probability you calculate based on your study or analysis.

A p-value less than or equal to your significance level (typically ≤ 0.05) is statistically significant.

A p-value less than or equal to a predetermined significance level (often 0.05 or 0.01) indicates a statistically significant result, meaning the observed data provide strong evidence against the null hypothesis.

This suggests the effect under study likely represents a real relationship rather than just random chance.

For instance, if you set α = 0.05, you would reject the null hypothesis if your p -value ≤ 0.05. 

It indicates strong evidence against the null hypothesis, as there is less than a 5% probability the null is correct (and the results are random).

Therefore, we reject the null hypothesis and accept the alternative hypothesis.

Example: Statistical Significance

Upon analyzing the pain relief effects of the new drug compared to the placebo, the computed p-value is less than 0.01, which falls well below the predetermined alpha value of 0.05. Consequently, you conclude that there is a statistically significant difference in pain relief between the new drug and the placebo.

What does a p-value of 0.001 mean?

A p-value of 0.001 is highly statistically significant beyond the commonly used 0.05 threshold. It indicates strong evidence of a real effect or difference, rather than just random variation.

Specifically, a p-value of 0.001 means there is only a 0.1% chance of obtaining a result at least as extreme as the one observed, assuming the null hypothesis is correct.

Such a small p-value provides strong evidence against the null hypothesis, leading to rejecting the null in favor of the alternative hypothesis.

A p-value more than the significance level (typically p > 0.05) is not statistically significant and indicates strong evidence for the null hypothesis.

This means we retain the null hypothesis and reject the alternative hypothesis. You should note that you cannot accept the null hypothesis; we can only reject it or fail to reject it.

Note : when the p-value is above your threshold of significance,  it does not mean that there is a 95% probability that the alternative hypothesis is true.

One-Tailed Test

Two-Tailed Test

How do you calculate the p-value ?

Most statistical software packages like R, SPSS, and others automatically calculate your p-value. This is the easiest and most common way.

Online resources and tables are available to estimate the p-value based on your test statistic and degrees of freedom.

These tables help you understand how often you would expect to see your test statistic under the null hypothesis.

Understanding the Statistical Test:

Different statistical tests are designed to answer specific research questions or hypotheses. Each test has its own underlying assumptions and characteristics.

For example, you might use a t-test to compare means, a chi-squared test for categorical data, or a correlation test to measure the strength of a relationship between variables.

Be aware that the number of independent variables you include in your analysis can influence the magnitude of the test statistic needed to produce the same p-value.

This factor is particularly important to consider when comparing results across different analyses.

Example: Choosing a Statistical Test

If you’re comparing the effectiveness of just two different drugs in pain relief, a two-sample t-test is a suitable choice for comparing these two groups. However, when you’re examining the impact of three or more drugs, it’s more appropriate to employ an Analysis of Variance ( ANOVA) . Utilizing multiple pairwise comparisons in such cases can lead to artificially low p-values and an overestimation of the significance of differences between the drug groups.

How to report

A statistically significant result cannot prove that a research hypothesis is correct (which implies 100% certainty).

Instead, we may state our results “provide support for” or “give evidence for” our research hypothesis (as there is still a slight probability that the results occurred by chance and the null hypothesis was correct – e.g., less than 5%).

Example: Reporting the results

In our comparison of the pain relief effects of the new drug and the placebo, we observed that participants in the drug group experienced a significant reduction in pain ( M = 3.5; SD = 0.8) compared to those in the placebo group ( M = 5.2; SD  = 0.7), resulting in an average difference of 1.7 points on the pain scale (t(98) = -9.36; p < 0.001).

The 6th edition of the APA style manual (American Psychological Association, 2010) states the following on the topic of reporting p-values:

“When reporting p values, report exact p values (e.g., p = .031) to two or three decimal places. However, report p values less than .001 as p < .001.

The tradition of reporting p values in the form p < .10, p < .05, p < .01, and so forth, was appropriate in a time when only limited tables of critical values were available.” (p. 114)

• Do not use 0 before the decimal point for the statistical value p as it cannot equal 1. In other words, write p = .001 instead of p = 0.001.
• Please pay attention to issues of italics ( p is always italicized) and spacing (either side of the = sign).
• p = .000 (as outputted by some statistical packages such as SPSS) is impossible and should be written as p < .001.
• The opposite of significant is “nonsignificant,” not “insignificant.”

Why is the p -value not enough?

A lower p-value  is sometimes interpreted as meaning there is a stronger relationship between two variables.

However, statistical significance means that it is unlikely that the null hypothesis is true (less than 5%).

To understand the strength of the difference between the two groups (control vs. experimental) a researcher needs to calculate the effect size .

When do you reject the null hypothesis?

In statistical hypothesis testing, you reject the null hypothesis when the p-value is less than or equal to the significance level (α) you set before conducting your test. The significance level is the probability of rejecting the null hypothesis when it is true. Commonly used significance levels are 0.01, 0.05, and 0.10.

Remember, rejecting the null hypothesis doesn’t prove the alternative hypothesis; it just suggests that the alternative hypothesis may be plausible given the observed data.

The p -value is conditional upon the null hypothesis being true but is unrelated to the truth or falsity of the alternative hypothesis.

What does p-value of 0.05 mean?

If your p-value is less than or equal to 0.05 (the significance level), you would conclude that your result is statistically significant. This means the evidence is strong enough to reject the null hypothesis in favor of the alternative hypothesis.

Are all p-values below 0.05 considered statistically significant?

No, not all p-values below 0.05 are considered statistically significant. The threshold of 0.05 is commonly used, but it’s just a convention. Statistical significance depends on factors like the study design, sample size, and the magnitude of the observed effect.

A p-value below 0.05 means there is evidence against the null hypothesis, suggesting a real effect. However, it’s essential to consider the context and other factors when interpreting results.

Researchers also look at effect size and confidence intervals to determine the practical significance and reliability of findings.

How does sample size affect the interpretation of p-values?

Sample size can impact the interpretation of p-values. A larger sample size provides more reliable and precise estimates of the population, leading to narrower confidence intervals.

With a larger sample, even small differences between groups or effects can become statistically significant, yielding lower p-values. In contrast, smaller sample sizes may not have enough statistical power to detect smaller effects, resulting in higher p-values.

Therefore, a larger sample size increases the chances of finding statistically significant results when there is a genuine effect, making the findings more trustworthy and robust.

Can a non-significant p-value indicate that there is no effect or difference in the data?

No, a non-significant p-value does not necessarily indicate that there is no effect or difference in the data. It means that the observed data do not provide strong enough evidence to reject the null hypothesis.

There could still be a real effect or difference, but it might be smaller or more variable than the study was able to detect.

Other factors like sample size, study design, and measurement precision can influence the p-value. It’s important to consider the entire body of evidence and not rely solely on p-values when interpreting research findings.

Can P values be exactly zero?

While a p-value can be extremely small, it cannot technically be absolute zero. When a p-value is reported as p = 0.000, the actual p-value is too small for the software to display. This is often interpreted as strong evidence against the null hypothesis. For p values less than 0.001, report as p < .001

Further Information

• P-values and significance tests (Kahn Academy)
• Hypothesis testing and p-values (Kahn Academy)
• Wasserstein, R. L., Schirm, A. L., & Lazar, N. A. (2019). Moving to a world beyond “ p “< 0.05”.
• Criticism of using the “ p “< 0.05”.
• Publication manual of the American Psychological Association
• Statistics for Psychology Book Download

Bland, J. M., & Altman, D. G. (1994). One and two sided tests of significance: Authors’ reply.  BMJ: British Medical Journal ,  309 (6958), 874.

Goodman, S. N., & Royall, R. (1988). Evidence and scientific research.  American Journal of Public Health ,  78 (12), 1568-1574.

Goodman, S. (2008, July). A dirty dozen: twelve p-value misconceptions . In  Seminars in hematology  (Vol. 45, No. 3, pp. 135-140). WB Saunders.

Lang, J. M., Rothman, K. J., & Cann, C. I. (1998). That confounded P-value.  Epidemiology (Cambridge, Mass.) ,  9 (1), 7-8.

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• Dorothy Anne Forbes
• Correspondence to Dorothy Anne Forbes Faculty of Nursing, University of Alberta, Level 3, Edmonton Clinic Health Academy, Edmonton, Alberta, T6G 1C9, Canada; dorothy.forbes{at}ualberta.ca

https://doi.org/10.1136/ebnurs-2012-100524

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Researchers aim to make the strongest possible conclusions from limited amounts of data. To do this, they need to overcome two problems. First, important differences in the findings can be obscured by natural variability and experimental imprecision. Thus, it is difficult to distinguish real differences from random variability. Second, researchers' natural inclination is to conclude that differences are real, and to minimise the contribution of random variability. Statistical probability minimises this from happening. 1

Statistical probability or p values reveal whether the findings in a research study are statistically significant, meaning that the findings are unlikely to have occurred by chance. To understand the p value concept, it is important to understand its relationship with the α level. Before conducting a study, researchers specify the α level which is most often set at 0.05 (5%). This conventional level was based on the writings of Sir Ronald Fisher, an influential statistician, who in 1926 reported that he preferred the 0.05 cut-off for separating the probable from the improbable. 2 Researchers who set α at 0.05 are willing to accept that there is a 5% chance that their findings are wrong. However, researchers may adopt probability cut-offs that are more generous (eg, an α set at 0.10 means there is a 10% chance that the conclusions are wrong) or more stringent (eg, an α set at 0.01 means there is a 1% chance that the conclusions are wrong). The design of the study, purpose or intuition may influence the researcher's setting of the α level. 2

To illustrate how setting the α level may affect the conclusions of a study, let us examine a research study that compared the annual incomes of hospital based nurses and community based nurses. The mean annual income for hospital based nurses was reported to be $70 000 and for community based nurses to be $60 000. The p value of this study was 0.08. If the researchers set the α level at 0.05, they would conclude that there was no significant difference between the annual incomes of hospital and community-based nurses, since the p value of 0.08 exceeded the α level of 0.05. However, if the α level had been set at 0.10, the p value of 0.08 would be less than the α level and the researchers would conclude that there was a significant difference between the annual incomes of hospital and community based nurses. Two very different conclusions. 3

It is easy to read far too much into the word significant because the statistical use of the word has a meaning entirely distinct from its usual meaning. Just because a difference is statistically significant does not mean that it is important or interesting. In the example above, at the 0.10 α level, although the findings are statistically significant, results due to chance occur 1 out of 10 times. Thus, chance of conclusion error is higher than when the α level is set at 0.05 and results due to chance occur 5 out of 100 times or 1 in 20 times. In the end, the reader must decide if the researchers selected the appropriate α level and whether the conclusions are meaningful or not.

• ↵ Graphpad . What is a p value ? 2011 . http://www.graphpad.com/articles/pvalue.htm (accessed 10 Dec 2011) .
• Munroe BH ,
• Jacobsen BS
• El-Masri MM

Competing interests None.

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What Is P-Value?

Understanding p-value.

• P-Value in Hypothesis Testing

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P-Value: What It Is, How to Calculate It, and Why It Matters

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In statistics, a p-value is a number that indicates how likely you are to obtain a value that is at least equal to or more than the actual observation if the null hypothesis is correct.

The p-value serves as an alternative to rejection points to provide the smallest level of significance at which the null hypothesis would be rejected. A smaller p-value means stronger evidence in favor of the alternative hypothesis.

P-value is often used to promote credibility for studies or reports by government agencies. For example, the U.S. Census Bureau stipulates that any analysis with a p-value greater than 0.10 must be accompanied by a statement that the difference is not statistically different from zero. The Census Bureau also has standards in place stipulating which p-values are acceptable for various publications.

Key Takeaways

• A p-value is a statistical measurement used to validate a hypothesis against observed data.
• A p-value measures the probability of obtaining the observed results, assuming that the null hypothesis is true.
• The lower the p-value, the greater the statistical significance of the observed difference.
• A p-value of 0.05 or lower is generally considered statistically significant.
• P-value can serve as an alternative to—or in addition to—preselected confidence levels for hypothesis testing.

Jessica Olah / Investopedia

P-values are usually found using p-value tables or spreadsheets/statistical software. These calculations are based on the assumed or known probability distribution of the specific statistic tested. P-values are calculated from the deviation between the observed value and a chosen reference value, given the probability distribution of the statistic, with a greater difference between the two values corresponding to a lower p-value.

Mathematically, the p-value is calculated using integral calculus from the area under the probability distribution curve for all values of statistics that are at least as far from the reference value as the observed value is, relative to the total area under the probability distribution curve.

The calculation for a p-value varies based on the type of test performed. The three test types describe the location on the probability distribution curve: lower-tailed test, upper-tailed test, or two-tailed test .

In a nutshell, the greater the difference between two observed values, the less likely it is that the difference is due to simple random chance, and this is reflected by a lower p-value.

The P-Value Approach to Hypothesis Testing

The p-value approach to hypothesis testing uses the calculated probability to determine whether there is evidence to reject the null hypothesis. The null hypothesis, also known as the conjecture, is the initial claim about a population (or data-generating process). The alternative hypothesis states whether the population parameter differs from the value of the population parameter stated in the conjecture.

In practice, the significance level is stated in advance to determine how small the p-value must be to reject the null hypothesis. Because different researchers use different levels of significance when examining a question, a reader may sometimes have difficulty comparing results from two different tests. P-values provide a solution to this problem.

Even a low p-value is not necessarily proof of statistical significance, since there is still a possibility that the observed data are the result of chance. Only repeated experiments or studies can confirm if a relationship is statistically significant.

For example, suppose a study comparing returns from two particular assets was undertaken by different researchers who used the same data but different significance levels. The researchers might come to opposite conclusions regarding whether the assets differ.

If one researcher used a confidence level of 90% and the other required a confidence level of 95% to reject the null hypothesis, and if the p-value of the observed difference between the two returns was 0.08 (corresponding to a confidence level of 92%), then the first researcher would find that the two assets have a difference that is statistically significant , while the second would find no statistically significant difference between the returns.

To avoid this problem, the researchers could report the p-value of the hypothesis test and allow readers to interpret the statistical significance themselves. This is called a p-value approach to hypothesis testing. Independent observers could note the p-value and decide for themselves whether that represents a statistically significant difference or not.

Example of P-Value

An investor claims that their investment portfolio’s performance is equivalent to that of the Standard & Poor’s (S&P) 500 Index . To determine this, the investor conducts a two-tailed test.

The null hypothesis states that the portfolio’s returns are equivalent to the S&P 500’s returns over a specified period, while the alternative hypothesis states that the portfolio’s returns and the S&P 500’s returns are not equivalent—if the investor conducted a one-tailed test , the alternative hypothesis would state that the portfolio’s returns are either less than or greater than the S&P 500’s returns.

The p-value hypothesis test does not necessarily make use of a preselected confidence level at which the investor should reset the null hypothesis that the returns are equivalent. Instead, it provides a measure of how much evidence there is to reject the null hypothesis. The smaller the p-value, the greater the evidence against the null hypothesis.

Thus, if the investor finds that the p-value is 0.001, there is strong evidence against the null hypothesis, and the investor can confidently conclude that the portfolio’s returns and the S&P 500’s returns are not equivalent.

Although this does not provide an exact threshold as to when the investor should accept or reject the null hypothesis, it does have another very practical advantage. P-value hypothesis testing offers a direct way to compare the relative confidence that the investor can have when choosing among multiple different types of investments or portfolios relative to a benchmark such as the S&P 500.

For example, for two portfolios, A and B, whose performance differs from the S&P 500 with p-values of 0.10 and 0.01, respectively, the investor can be much more confident that portfolio B, with a lower p-value, will actually show consistently different results.

Is a 0.05 P-Value Significant?

A p-value less than 0.05 is typically considered to be statistically significant, in which case the null hypothesis should be rejected. A p-value greater than 0.05 means that deviation from the null hypothesis is not statistically significant, and the null hypothesis is not rejected.

What Does a P-Value of 0.001 Mean?

A p-value of 0.001 indicates that if the null hypothesis tested were indeed true, then there would be a one-in-1,000 chance of observing results at least as extreme. This leads the observer to reject the null hypothesis because either a highly rare data result has been observed or the null hypothesis is incorrect.

How Can You Use P-Value to Compare 2 Different Results of a Hypothesis Test?

If you have two different results, one with a p-value of 0.04 and one with a p-value of 0.06, the result with a p-value of 0.04 will be considered more statistically significant than the p-value of 0.06. Beyond this simplified example, you could compare a 0.04 p-value to a 0.001 p-value. Both are statistically significant, but the 0.001 example provides an even stronger case against the null hypothesis than the 0.04.

The p-value is used to measure the significance of observational data. When researchers identify an apparent relationship between two variables, there is always a possibility that this correlation might be a coincidence. A p-value calculation helps determine if the observed relationship could arise as a result of chance.

U.S. Census Bureau. “ Statistical Quality Standard E1: Analyzing Data .”

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Section 2.1: p Values

Learning Objectives

At the end of this section you should be able to answer the following questions:

• What is a p value?
• How can you interpret a p value?
• What question can p value answer?

An important area of statistics is probability, and it is the basis for all of the tests we will be reviewing in this textbook. One important kind of probability is a conditional probability. For example, given the weather forecast for today, what is the likelihood that it will rain?

The p value itself is a figure or numeral – typically represented by a number between 0 and 1.00 – that provides the probability of a result (for a particular test statistic) being due to a true effect rather than chance. The p value is a conditional probability and relies on a number of assumptions about the test statistics used.

Here is an example from psychology that provides an illustration of the p value:

Psychological scientists at your university are evaluating a clinical therapy that is believed to reduce anxiety in young adults. In a field study, these scientists use two groups to test the therapy – one group receives the clinical therapy and a second group that does not receive the clinical therapy – which are respectively known as the experimental and control groups. Anxiety in participants is then measured in both groups after the therapy takes place (or not). Using a T-test statistic – which examines the different means for anxiety between two groups – the result of the test statistic is t(18)= 2.7, p = .01.

The p value is indicated by the statement of p = .01 that appears after the 2.7, which is the value of the T-test statistic. You interpret the significance of a p value based on a critical value for p values, which is often designated as .05. You also note that p values less than .05 are considered significant in most research. In our example p = .01 which is below .05. This means that the test statistic of t(18)= 2.7 provides evidence for a difference between the control and experimental groups.

When concluding there is a difference between the control and experimental groups, a researcher is really referring back to the populations from which the two groups are assumed to be drawn. Hence, there is an inference from the samples back to the populations.

It is critical to remember that a p value does NOT answer “What is the probability that the difference is due to chance?” A p value does answer: ‘Assuming that there is no real difference in the populations (that correspond to the two groups), what is the probability that the difference between the means of randomly selected subjects will be as large as or larger than actually observed?’ This distinction might sound academic, but it is very important.

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What is P-Value? – Understanding the meaning, math and methods

• October 12, 2019
• Selva Prabhakaran

P Value is a probability score that is used in statistical tests to establish the statistical significance of an observed effect. Though p-values are commonly used, the definition and meaning is often not very clear even to experienced Statisticians and Data Scientists. In this post I will attempt to explain the intuition behind p-value as clear as possible.

Introduction

In Data Science interviews, one of the frequently asked questions is ‘What is P-Value?”.

Believe it or not, even experienced Data Scientists often fail to answer this question. This is partly because of the way statistics is taught and the definitions available in textbooks and online sources.

According to American Statistical Association, “a p-value is the probability under a specified statistical model that a statistical summary of the data (e.g., the sample mean difference between two compared groups) would be equal to or more extreme than its observed value.”

That’s hard to grasp, yes?

Alright, lets understand what really is p value in small meaningful pieces so ultimately it all makes sense.

When and how is p-value used?

To understand p-value, you need to understand some background and context behind it. So, let’s start with the basics.

p-values are often reported whenever you perform a statistical significance test (like t-test, chi-square test etc). These tests typically return a computed test statistic and the associated p-value. This reported value is used to establish the statistical significance of the relationships being tested.

So, whenever you see a p-value, there is an associated statistical test.

That means, there is a Hypothesis testing being conducted with a defined Null Hypothesis (H0) and a corresponding Alternate hypothesis (HA).

The p-value reported is used to make a decision on whether the null hypothesis being tested can be rejected or not.

Let’s understand a little bit more about the null and alternate hypothesis.

Now, how to frame a Null hypothesis in general?

While the null hypothesis itself changes with every statistical test, there is a general principle to frame it:

The null hypothesis assumes there is ‘no effect’ or ‘relationship’ by default .

For example: if you are testing if a drug treatment is effective or not, then the null hypothesis will assume there is not difference in outcome between the treated and untreated groups. Likewise, if you are testing if one variable influences another (say, car weight influences the mileage), then null hypothesis will postulate there is no relationship between the two.

It simply implies the absence of an effect.

Examples of Statistical Tests reporting out p-value

Here are some examples of Null hypothesis (H0) for popular statistical tests:

• Welch Two Sample t-Test: The true difference in means of two samples is equal to 0
• Linear Regression: The beta coefficient(slope) of the X variable is zero
• Chi Square test: There is no difference between expected frequencies and observed frequencies.

Get the feel?

But how would the alternate hypothesis would look like?

The alternate hypothesis (HA) is always framed to negate the null hypothesis. The corresponding HA for above tests are as follows:

• Welch Two Sample t-Test: The true difference in means of two samples is NOT equal to 0
• Linear Regression: The beta coefficient(slope) of the X variable is NOT zero
• Chi Square test: The difference between expected frequencies and observed frequencies is NOT zero.

What p-value really is

Now, back to the discussion on p-value.

Along with every statistical test, you will get a corresponding p-value in the results output.

What is this meant for?

It is used to determine if the data is statistically incompatible with the null hypothesis.

Not clear eh?

Let me put it in another way.

The P Value basically helps to answer the question: ‘Does the data really represent the observed effect?’.

This leads us to a more mathematical definition of P-Value.

The P Value is the probability of seeing the effect(E) when the null hypothesis is true .

If you think about it, we want this probability to be very low.

Having said that, it is important to remember that p-value refers to not only what we observed but also observations more extreme than what was observed. That is why the formal definition of p-value contain the statement ‘would be equal to or more extreme than its observed value.’

How is p-value used to establish statistical significance

Now that you know, p value measures the probability of seeing the effect when the null hypothesis is true.

A sufficiently low value is required to reject the null hypothesis.

Notice how I have used the term ‘Reject the Null Hypothesis’ instead of stating the ‘Alternate Hypothesis is True’.

That’s because, we have tested the effect against the null hypothesis only.

So, when the p-value is low enough, we reject the null hypothesis and conclude the observed effect holds.

But how low is ‘low enough’ for rejecting the null hypothesis?

This level of ‘low enough’ cutoff is called the alpha level, and you need to decide it before conducting a statistical test.

But how low is ‘low enough’?

Practical Guidelines to set the cutoff of Statistical Significance (alpha level)

Let’s first understand what is Alpha level.

It is the cutoff probability for p-value to establish statistical significance for a given hypothesis test. For an observed effect to be considered as statistically significant, the p-value of the test should be lower than the pre-decided alpha value.

Typically for most statistical tests(but not always), alpha is set as 0.05.

In which case, it has to be less than 0.05 to be considered as statistically significant.

What happens if it is say, 0.051?

It is still considered as not significant. We do NOT call it as a weak statistical significant. It is either black or white. There is no gray with respect to statistical significance.

Now, how to set the alpha level?

Well, the usual practice is to set it to 0.05.

But when the occurrence of the event is rare, you may want to set a very low alpha. The rarer it is, the lower the alpha.

For example in the CERN’s Hadron collider experiment to detect Higgs-Boson particles(which was very rare), the alpha level was set so low to 5 Sigma levels , which means a p value of less than 3 * 10^-7 is required reject the null hypothesis.

Whereas for a more likely event, it can go up to 0.1.

Secondly, more the samples (number of observations) you have the lower should be the alpha level. Because, even a small effect can be made to produce a lower p-value just by increasing the number of observations. The opposite is also true, that is, a large effect can be made to produce high p value by reducing the sample size.

In case you don’t know how likely the event can occur, its a common practice to set it as 0.05. But, as a thumb rule, never set the alpha greater than 0.1.

Having said that the alpha=0.05 is mostly an arbitrary choice. Then why do most people still use p=0.05? That’s because thats what is taught in college courses and being traditionally used by the scientific community and publishers.

What P Value is Not

Given the uncertainty around the meaning of p-value, it is very common to misinterpret and use it incorrectly.

Some of the common misconceptions are as follows:

• P-Value is the probability of making a mistake. Wrong!
• P-Value measures the importance of a variable. Wrong!
• P-Value measures the strength of an effect. Wrong!

A smaller p-value does not signify the variable is more important or even a stronger effect.

Because, like I mentioned earlier, any effect no matter how small can be made to produce smaller p-value only by increasing the number of observations (sample size).

Likewise, a larger value does not imply a variable is not important.

For a sound communication, it is necessary to report not just the p-value but also the sample size along with it. This is especially necessary if the experiments involve different sample sizes.

Secondly, making inferences and business decisions should not be based only on the p-value being lower than the alpha level.

Analysts should understand the business sense, understand the larger picture and bring out the reasoning before making an inference and not just rely on the p-value to make the inference for you.

Does this mean the p-value is not useful anymore?

Not really. It is a useful tool because it provides an objective standard for everyone to assess. Its just that you need to use it the right way.

Example: How to find p-value for linear regression

Linear regression is a traditional statistical modeling algorithm that is used to predict a continuous variable (a.k.a dependent variable) using one or more explanatory variables.

Let’s see an example of extracting the p-value with linear regression using the mtcars dataset. In this dataset the specifications of the vehicle and the mileage performance is recorded.

We want to use linear regression to test if one of the specs “the ‘weight’ ( wt ) of the vehicle” has a significant relationship (linear) with the ‘mileage’ ( mpg ).

This can be conveniently done using python’s statsmodels library. But first, let’s load the data.

With statsmodels library

The X( wt ) and Y ( mpg ) variables are ready.

Null Hypothesis (H0): The slope of the line of best fit (a.k.a beta coefficient) is zero Alternate Hypothesis (H1): The beta coefficient is not zero.

To implement the test, use the smf.ols() function available in the formula.api of statsmodels . You can pass in the formula itself as the first argument and call fit() to train the linear model.

Once model is trained, call model.summary() to get a comprehensive view of the statistics.

The p-value is located in under the P>|t| against wt row. If you want to extract that value into a variable, use model.pvalues .

Since the p-value is much lower than the significance level (0.01), we reject the null hypothesis that the slope is zero and take that the data really represents the effect.

Well, that was just one example of computing p-value.

Whereas p-value can be associated with numerous statistical tests. If you are interested in finding out more about how it is used, see more examples of statistical tests with p-values.

In this post we covered what exactly is a p-value and how and how not to use it. We also saw a python example related to computing the p-value associated with linear regression.

Now with this understanding, let’s conclude what is the difference between Statistical Model from Machine Learning model?

Well, while both statistical as well as machine learning models are associated with making predictions, there can be many differences between these two. But most simply put, any predictive model that has p-values associated with it are considered as statistical model.

Happy learning!

To understand how exactly the P-value is computed, check out the example using the T-Test .

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Correlation – connecting the dots, the role of correlation in data analysis, hypothesis testing – a deep dive into hypothesis testing, the backbone of statistical inference, sampling and sampling distributions – a comprehensive guide on sampling and sampling distributions, law of large numbers – a deep dive into the world of statistics, central limit theorem – a deep dive into central limit theorem and its significance in statistics, skewness and kurtosis – peaks and tails, understanding data through skewness and kurtosis”, similar articles, complete introduction to linear regression in r, how to implement common statistical significance tests and find the p value, logistic regression – a complete tutorial with examples in r.

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Six Things to Know about P-Values

Whenever I write a research report, I feel strongly ambivalent about flagging data as “statistically significant.” If possible, I try to avoid it altogether. Why? Because p-values and concepts of statistical significance are often misunderstood, misused, and misleading. Indeed, the problem is so prevalent that one well-respected scientific journal in social psychology ( Basic and Applied Social Psychology ) has now banned “null hypothesis significance testing procedures” from articles that are submitted for review and publication.

Not surprisingly, this set off a firestorm of debate. Should social research (and by extension, market research) be using p-values and tests of significance? Last month, the American Statistical Association weighed in on the debate with an official statement about p-values. It lays out a clear definition of p-value and six guiding principles that ought to govern any decision to use p-values or not.

We agree with it wholeheartedly, and here we summarize the crux of the ASA’s statement:

A p-value is the probability under a specified statistical model that a statistical summary of the data (for example, the sample mean difference between two compared groups) would be equal to or more extreme than its observed value.

P-values should be used (or not) in accordance with the following six principles:

• P-values can indicate how incompatible the data are with a specified statistical model. But it is just one approach to measuring incompatibility, and it is only accurate to the extent that each of the underlying assumptions in calculating the value are true.
• P-values do not measure the probability that the studied hypothesis is true, or the probability that the data were produced by random chance alone. Rather, p-values measure the relationship between a hypothesis and the observed data, nothing more.
• Scientific conclusions and business or policy decisions should not be based only on whether a p-value passes a specific threshold. Context is critical, and that context includes the design of the research, the quality of the measurement, and any deviations from theoretical assumptions.
• Proper inference requires full reporting and transparency. As such, researchers should disclose the number of hypotheses explored during the study, all data collection decisions, all statistical analyses conducted, and all p-values computed.
• A p-value, or statistical significance, does not measure the size of an effect or the importance of a result. Statistical significance is not equivalent to scientific, human, or economic significance. Smaller p-values do not necessarily imply the presence of larger or more important effects, and larger p-values do not imply a lack of importance or even lack of effect.
• By itself, a p-value does not provide a good measure of evidence regarding a model or hypothesis.

Despite my ambivalence, I do rely on various tests of significance all the time in my work. Even if they violate key theoretical assumptions, they give me a feel for the amount of variation underlying the point estimates, all of which is incredibly useful as I analyze and interpret data. But then when I need to report all of that, I proceed with as much caution as our clients will allow. I urge you to do the same.

By Joe Hopper, Ph.D.

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• What Is Quantitative Research? | Definition, Uses & Methods

What Is Quantitative Research? | Definition, Uses & Methods

Published on June 12, 2020 by Pritha Bhandari . Revised on June 22, 2023.

Quantitative research is the process of collecting and analyzing numerical data. It can be used to find patterns and averages, make predictions, test causal relationships, and generalize results to wider populations.

Quantitative research is the opposite of qualitative research , which involves collecting and analyzing non-numerical data (e.g., text, video, or audio).

Quantitative research is widely used in the natural and social sciences: biology, chemistry, psychology, economics, sociology, marketing, etc.

• What is the demographic makeup of Singapore in 2020?
• How has the average temperature changed globally over the last century?
• Does environmental pollution affect the prevalence of honey bees?
• Does working from home increase productivity for people with long commutes?

Table of contents

Quantitative research methods, quantitative data analysis, advantages of quantitative research, disadvantages of quantitative research, other interesting articles, frequently asked questions about quantitative research.

You can use quantitative research methods for descriptive, correlational or experimental research.

• In descriptive research , you simply seek an overall summary of your study variables.
• In correlational research , you investigate relationships between your study variables.
• In experimental research , you systematically examine whether there is a cause-and-effect relationship between variables.

Correlational and experimental research can both be used to formally test hypotheses , or predictions, using statistics. The results may be generalized to broader populations based on the sampling method used.

To collect quantitative data, you will often need to use operational definitions that translate abstract concepts (e.g., mood) into observable and quantifiable measures (e.g., self-ratings of feelings and energy levels).

Note that quantitative research is at risk for certain research biases , including information bias , omitted variable bias , sampling bias , or selection bias . Be sure that you’re aware of potential biases as you collect and analyze your data to prevent them from impacting your work too much.

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Once data is collected, you may need to process it before it can be analyzed. For example, survey and test data may need to be transformed from words to numbers. Then, you can use statistical analysis to answer your research questions .

Descriptive statistics will give you a summary of your data and include measures of averages and variability. You can also use graphs, scatter plots and frequency tables to visualize your data and check for any trends or outliers.

Using inferential statistics , you can make predictions or generalizations based on your data. You can test your hypothesis or use your sample data to estimate the population parameter .

First, you use descriptive statistics to get a summary of the data. You find the mean (average) and the mode (most frequent rating) of procrastination of the two groups, and plot the data to see if there are any outliers.

You can also assess the reliability and validity of your data collection methods to indicate how consistently and accurately your methods actually measured what you wanted them to.

Quantitative research is often used to standardize data collection and generalize findings . Strengths of this approach include:

• Replication

Repeating the study is possible because of standardized data collection protocols and tangible definitions of abstract concepts.

• Direct comparisons of results

The study can be reproduced in other cultural settings, times or with different groups of participants. Results can be compared statistically.

• Large samples

Data from large samples can be processed and analyzed using reliable and consistent procedures through quantitative data analysis.

• Hypothesis testing

Using formalized and established hypothesis testing procedures means that you have to carefully consider and report your research variables, predictions, data collection and testing methods before coming to a conclusion.

Despite the benefits of quantitative research, it is sometimes inadequate in explaining complex research topics. Its limitations include:

• Superficiality

Using precise and restrictive operational definitions may inadequately represent complex concepts. For example, the concept of mood may be represented with just a number in quantitative research, but explained with elaboration in qualitative research.

• Narrow focus

Predetermined variables and measurement procedures can mean that you ignore other relevant observations.

• Structural bias

Despite standardized procedures, structural biases can still affect quantitative research. Missing data , imprecise measurements or inappropriate sampling methods are biases that can lead to the wrong conclusions.

• Lack of context

Quantitative research often uses unnatural settings like laboratories or fails to consider historical and cultural contexts that may affect data collection and results.

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

• Chi square goodness of fit test
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Inclusion and exclusion criteria

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

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

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 organizations.

Operationalization 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, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

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

Reliability and validity are both about how well a method measures something:

• Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
• Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research, you also have to consider the internal and external validity of your experiment.

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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A p-value is a statistical metric used to assess a hypothesis by comparing it with observed data.

This article delves into the concept of p-value, its calculation, interpretation, and significance. It also explores the factors that influence p-value and highlights its limitations.

Table of Content

• What is P-value?

How P-value is calculated?

How to interpret p-value, p-value in hypothesis testing, implementing p-value in python, applications of p-value, what is the p-value.

The p-value, or probability value, is a statistical measure used in hypothesis testing to assess the strength of evidence against a null hypothesis. It represents the probability of obtaining results as extreme as, or more extreme than, the observed results under the assumption that the null hypothesis is true.

In simpler words, it is used to reject or support the null hypothesis during hypothesis testing. In data science, it gives valuable insights on the statistical significance of an independent variable in predicting the dependent variable. 

Calculating the p-value typically involves the following steps:

• Formulate the Null Hypothesis (H0) : Clearly state the null hypothesis, which typically states that there is no significant relationship or effect between the variables.
• Choose an Alternative Hypothesis (H1) : Define the alternative hypothesis, which proposes the existence of a significant relationship or effect between the variables.
• Determine the Test Statistic : Calculate the test statistic, which is a measure of the discrepancy between the observed data and the expected values under the null hypothesis. The choice of test statistic depends on the type of data and the specific research question.
• Identify the Distribution of the Test Statistic : Determine the appropriate sampling distribution for the test statistic under the null hypothesis. This distribution represents the expected values of the test statistic if the null hypothesis is true.
• Calculate the Critical-value : Based on the observed test statistic and the sampling distribution, find the probability of obtaining the observed test statistic or a more extreme one, assuming the null hypothesis is true.
• Interpret the results: Compare the critical-value with t-statistic. If the t-statistic is larger than the critical value, it provides evidence to reject the null hypothesis, and vice-versa.

Its interpretation depends on the specific test and the context of the analysis. Several popular methods for calculating test statistics that are utilized in p-value calculations.

In general, a small p-value indicates that the observed data is unlikely to have occurred by random chance alone, which leads to the rejection of the null hypothesis. However, it’s crucial to choose the appropriate test based on the nature of the data and the research question, as well as to interpret the p-value in the context of the specific test being used.

The table given below shows the importance of p-value and shows the various kinds of errors that occur during hypothesis testing.

Type I error: Incorrect rejection of the null hypothesis. It is denoted by α (significance level). Type II error: Incorrect acceptance of the null hypothesis. It is denoted by β (power level)

Let’s consider an example to illustrate the process of calculating a p-value for Two Sample T-Test:

A researcher wants to investigate whether there is a significant difference in mean height between males and females in a population of university students.

Suppose we have the following data:

Starting with interpreting the process of calculating p-value

Step 1 : Formulate the Null Hypothesis (H0):

H0: There is no significant difference in mean height between males and females.

Step 2 : Choose an Alternative Hypothesis (H1):

H1: There is a significant difference in mean height between males and females.

Step 3 : Determine the Test Statistic:

The appropriate test statistic for this scenario is the two-sample t-test, which compares the means of two independent groups.

The t-statistic is a measure of the difference between the means of two groups relative to the variability within each group. It is calculated as the difference between the sample means divided by the standard error of the difference. It is also known as the t-value or t-score.

• s1 = First sample’s standard deviation
• s2 = Second sample’s standard deviation
• n1 = First sample’s sample size
• n2 = Second sample’s sample size

So, the calculated two-sample t-test statistic (t) is approximately 5.13.

Step 4 : Identify the Distribution of the Test Statistic:

The t-distribution is used for the two-sample t-test . The degrees of freedom for the t-distribution are determined by the sample sizes of the two groups.

 The t-distribution is a probability distribution with tails that are thicker than those of the normal distribution.

• where, n1 is total number of values for 1st category.
• n2 is total number of values for 2nd category.

The degrees of freedom (63) represent the variability available in the data to estimate the population parameters. In the context of the two-sample t-test, higher degrees of freedom provide a more precise estimate of the population variance, influencing the shape and characteristics of the t-distribution.

T-Statistic

The t-distribution is symmetric and bell-shaped, similar to the normal distribution. As the degrees of freedom increase, the t-distribution approaches the shape of the standard normal distribution. Practically, it affects the critical values used to determine statistical significance and confidence intervals.

Step 5 : Calculate Critical Value.

To find the critical t-value with a t-statistic of 5.13 and 63 degrees of freedom, we can either consult a t-table or use statistical software.

We can use scipy.stats module in Python to find the critical t-value using below code.

Comparing with T-Statistic:

The larger t-statistic suggests that the observed difference between the sample means is unlikely to have occurred by random chance alone. Therefore, we reject the null hypothesis.

• p ≤ (α = 0.05) : Reject the null hypothesis. There is sufficient evidence to conclude that the observed effect or relationship is statistically significant, meaning it is unlikely to have occurred by chance alone.
• p > (α = 0.05) : reject alternate hypothesis (or accept null hypothesis). The observed effect or relationship does not provide enough evidence to reject the null hypothesis. This does not necessarily mean there is no effect; it simply means the sample data does not provide strong enough evidence to rule out the possibility that the effect is due to chance.

In case the significance level is not specified, consider the below general inferences while interpreting your results. 

• If p > .10: not significant
• If p ≤ .10: slightly significant
• If p ≤ .05: significant
• If p ≤ .001: highly significant

Graphically, the p-value is located at the tails of any confidence interval. [As shown in fig 1]

Fig 1: Graphical Representation 

What influences p-value?

The p-value in hypothesis testing is influenced by several factors:

• Sample Size : Larger sample sizes tend to yield smaller p-values, increasing the likelihood of detecting significant effects.
• Effect Size: A larger effect size results in smaller p-values, making it easier to detect a significant relationship.
• Variability in the Data : Greater variability often leads to larger p-values, making it harder to identify significant effects.
• Significance Level : A lower chosen significance level increases the threshold for considering p-values as significant.
• Choice of Test: Different statistical tests may yield different p-values for the same data.
• Assumptions of the Test : Violations of test assumptions can impact p-values.

Understanding these factors is crucial for interpreting p-values accurately and making informed decisions in hypothesis testing.

Significance of P-value

• The p-value provides a quantitative measure of the strength of the evidence against the null hypothesis.
• Decision-Making in Hypothesis Testing
• P-value serves as a guide for interpreting the results of a statistical test. A small p-value suggests that the observed effect or relationship is statistically significant, but it does not necessarily mean that it is practically or clinically meaningful.

Limitations of P-value

• The p-value is not a direct measure of the effect size, which represents the magnitude of the observed relationship or difference between variables. A small p-value does not necessarily mean that the effect size is large or practically meaningful.
• Influenced by Various Factors

The p-value is a crucial concept in statistical hypothesis testing, serving as a guide for making decisions about the significance of the observed relationship or effect between variables.

Let’s consider a scenario where a tutor believes that the average exam score of their students is equal to the national average (85). The tutor collects a sample of exam scores from their students and performs a one-sample t-test to compare it to the population mean (85).

• The code performs a one-sample t-test to compare the mean of a sample data set to a hypothesized population mean.
• It utilizes the scipy.stats library to calculate the t-statistic and p-value. SciPy is a Python library that provides efficient numerical routines for scientific computing.
• The p-value is compared to a significance level (alpha) to determine whether to reject the null hypothesis.

Since, 0.7059>0.05 , we would conclude to fail to reject the null hypothesis. This means that, based on the sample data, there isn’t enough evidence to claim a significant difference in the exam scores of the tutor’s students compared to the national average. The tutor would accept the null hypothesis, suggesting that the average exam score of their students is statistically consistent with the national average.

• During Forward and Backward propagation: When fitting a model (say a Multiple Linear Regression model), we use the p-value in order to find the most significant variables that contribute significantly in predicting the output.
• Effects of various drug medicines: It is highly used in the field of medical research in determining whether the constituents of any drug will have the desired effect on humans or not. P-value is a very strong statistical tool used in hypothesis testing. It provides a plethora of valuable information while making an important decision like making a business intelligence inference or determining whether a drug should be used on humans or not, etc. For any doubt/query, comment below.

The p-value is a crucial concept in statistical hypothesis testing, providing a quantitative measure of the strength of evidence against the null hypothesis. It guides decision-making by comparing the p-value to a chosen significance level, typically 0.05. A small p-value indicates strong evidence against the null hypothesis, suggesting a statistically significant relationship or effect. However, the p-value is influenced by various factors and should be interpreted alongside other considerations, such as effect size and context.

Frequently Based Questions (FAQs)

Why is p-value greater than 1.

A p-value is a probability, and probabilities must be between 0 and 1. Therefore, a p-value greater than 1 is not possible.

What does P 0.01 mean?

It means that the observed test statistic is unlikely to occur by chance if the null hypothesis is true. It represents a 1% chance of observing the test statistic or a more extreme one under the null hypothesis.

Is 0.9 a good p-value?

A good p-value is typically less than or equal to 0.05, indicating that the null hypothesis is likely false and the observed relationship or effect is statistically significant.

What is p-value in a model?

It is a measure of the statistical significance of a parameter in the model. It represents the probability of obtaining the observed value of the parameter or a more extreme one, assuming the null hypothesis is true.

Why is p-value so low?

A low p-value means that the observed test statistic is unlikely to occur by chance if the null hypothesis is true. It suggests that the observed relationship or effect is statistically significant and not due to random sampling variation.

How Can You Use P-value to Compare Two Different Results of a Hypothesis Test?

Compare p-values: Lower p-value indicates stronger evidence against null hypothesis, favoring results with smaller p-values in hypothesis testing.

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