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In statistics, the p-value is the probability that, using a given statistical model, the statistical summary (such as the sample mean difference between two compared groups) would be the same as or more extreme than the actual observed results.[1] Statistical hypothesis testing making use of p-values is commonly used in many fields of research[2] such as economics, finance, political science, psychology,[3] biology, criminal justice, criminology, and sociology.[4] Their misuse has been a matter of considerable controversy.

Overview and controversy[edit]

The p-value is defined as the probability of obtaining a result equal to or "more extreme" than what was actually observed, when the null hypothesis is true.[5][6]

In frequentist inference, the p-value is widely used in statistical hypothesis testing, specifically in null hypothesis significance testing. In this method, as part of experimental design, before performing the experiment, one first chooses a model (the null hypothesis) and a threshold value for p, called the significance level of the test, traditionally 5% or 1% [7] and denoted as α. If the p-value is less than or equal to the chosen significance level (α), the test suggests that the observed data is inconsistent with the null hypothesis, so the null hypothesis must be rejected. However, that does not prove that the tested hypothesis is true. When the p-value is calculated correctly, this test guarantees that the Type I error rate is at most α. For typical analysis, using the standard α = 0.05 cutoff, the null hypothesis is rejected when p < .05 and not rejected when p > .05. The p-value does not, in itself, support reasoning about the probabilities of hypotheses but is only a tool for deciding whether to reject the null hypothesis.

The American Statistical Association, in a statement on the use of p-values,[8] affirmed the usefulness of properly interpreted p-values, but cautioned that p-values are "commonly misused and misinterpreted." The use of bright-line rules as cutoffs, such as p ≤ 0.05, without other supporting statistical evidence, was particularly criticized:[8]


While there is widespread agreement that p-values are often misused,[9][10] there is no consensus on alternatives.[11]

Basic concepts[edit]

The p-value is used in the context of null hypothesis testing in order to quantify the idea of statistical significance of evidence.[a] Null hypothesis testing is a reductio ad absurdum argument adapted to statistics. In essence, a claim is shown to be valid by demonstrating the improbability of the consequence that results from assuming the counter-claim to be true.

As such, the only hypothesis that needs to be specified in this test and which embodies the counter-claim is referred to as the null hypothesis. A result is said to be statistically significant if it allows us to reject the null hypothesis. That is, as per the reductio ad absurdum reasoning, the statistically significant result should be highly improbable if the null hypothesis is assumed to be true. The rejection of the null hypothesis implies that the correct hypothesis lies in the logical complement of the null hypothesis. However, unless there is a single alternative to the null hypothesis, the rejection of null hypothesis does not tell us which of the alternatives might be the correct one.

For instance, if the null hypothesis is assumed to be a standard normal distribution N(0,1), the rejection of this null hypothesis can either mean (i) the mean is not zero, or (ii) the variance is not unity, or (iii) the distribution is not normal, depending on the type of test performed. However, supposing we manage to reject the zero mean hypothesis, even if we know the distribution is normal and variance is unity, the null hypothesis test does not tell us which non-zero value we should adopt as the new mean.

In statistics, a statistical hypothesis refers to a probability distribution that is assumed to govern the observed data.[b] If is a random variable representing the observed data and is the statistical hypothesis under consideration, then the notion of statistical significance can be naively quantified by the conditional probability , which gives the likelihood of the observation if the hypothesis is assumed to be correct. However, if is a continuous random variable and an instance is observed, Thus, this naive definition is inadequate and needs to be changed so as to accommodate the continuous random variables.

Nonetheless, it helps to clarify that p-values should not be confused with probability on hypothesis (as is done in Bayesian Hypothesis Testing) such as the probability of the hypothesis given the data, or the probability of the hypothesis being true, or the probability of observing the given data.

Definition and interpretation[edit]


The p-value is defined as the probability, under the assumption of hypothesis , of obtaining a result equal to or more extreme than what was actually observed. Depending on how it is looked at, the "more extreme than what was actually observed" can mean (right-tail event) or (left-tail event) or the "smaller" of and (double-tailed event). Thus, the p-value is given by

  • for right tail event,
  • for left tail event,
  • for double tail event.

The smaller the p-value, the larger the significance because it tells the investigator that the hypothesis under consideration may not adequately explain the observation. The hypothesis is rejected if any of these probabilities is less than or equal to a small, fixed but arbitrarily pre-defined threshold value , which is referred to as the level of significance. Unlike the p-value, the level is not derived from any observational data and does not depend on the underlying hypothesis; the value of is instead determined by the consensus of the research community that the investigator is working in.




This also means that if we fix an instantiation of p-value and allow to vary over , we can obtain an equivalent interpretation of p-value in terms of level as the lowest value of that can be assumed for which the null hypothesis can be rejected for a given set of observations. Obviously, assuming an smaller than the instantiated p-value will end up not rejecting the null hypothesis.


Usually, instead of the actual observations, is instead a test statistic. A test statistic is a scalar function of all the observations, such as the average or the correlation coefficient, which summarizes the characteristics of the data by a single number, relevant to a particular inquiry. As such, the test statistic follows a distribution determined by the function used to define that test statistic and the distribution of the input observational data.





One roll of a pair of dice[edit]

Suppose a researcher rolls a pair of dice once and assumes a null hypothesis that the dice are fair, not loaded or weighted toward any specific number/roll/result; uniform. The test statistic is "the sum of the rolled numbers" and is one-tailed. The researcher rolls the dice and observes that both dice show 6, yielding a test statistic of 12. The p-value of this outcome is 1/36 (because under the assumption of the null hypothesis, the test statistic is uniformly distributed) or about 0.028 (the highest test statistic out of 6×6 = 36 possible outcomes). If the researcher assumed a significance level of 0.05, this result would be deemed significant and the hypothesis that the dice are fair would be rejected.


Five heads in a row[edit]

Suppose a researcher flips a coin five times in a row and assumes a null hypothesis that the coin is fair. The test statistic of "total number of heads" can be one-tailed or two-tailed: a one-tailed test corresponds to seeing if the coin is biased towards heads, but a two-tailed test corresponds to seeing if the coin is biased either way. The researcher flips the coin five times and observes heads each time (HHHHH), yielding a test statistic of 5. In a one-tailed test, this is the most extreme value out of all possible outcomes, and yields a p-value of (1/2)5 = 1/32 ≈ 0.03. If the researcher assumed a significance level of 0.05, this result would be deemed significant and the hypothesis that the coin is fair would be rejected. In a two-tailed test, a test statistic of zero heads (TTTTT) is just as extreme and thus the data of HHHHH would yield a p-value of 2×(1/2)5 = 1/16 ≈ 0.06, which is not significant at the 0.05 level.


Sample size dependence[edit]


In both cases the data suggest that the null hypothesis is false (that is, the coin is not fair somehow), but changing the sample size changes the p-value. In the first case, the sample size is not large enough to allow the null hypothesis to be rejected at the 0.05 level (in fact, the p-value can never be below 0.05 for the coin example).


Alternating coin flips[edit]


Suppose instead that the test statistic for this experiment was the "number of alternations" (that is, the number of times when H followed T or T followed H), which is again two-tailed. That would yield a test statistic of 9, which is extreme and has a p-value of . That would be considered extremely significant, well beyond the 0.05 level. These data indicate that, in terms of one test statistic, the data set is extremely unlikely to have occurred by chance, but it does not suggest that the coin is biased towards heads or tails.

By the first test statistic, the data yield a high p-value, suggesting that the number of heads observed is not unlikely. By the second test statistic, the data yield a low p-value, suggesting that the pattern of flips observed is very, very unlikely. There is no "alternative hypothesis" (so only rejection of the null hypothesis is possible) and such data could have many causes. The data may instead be forged, or the coin may be flipped by a magician who intentionally alternated outcomes.

This example demonstrates that the p-value depends completely on the test statistic used and illustrates that p-values can only help researchers to reject a null hypothesis, not consider other hypotheses.

Coin flipping[edit]


Suppose that the experimental results show the coin turning up heads 14 times out of 20 total flips. The null hypothesis is that the coin is fair, and the test statistic is the number of heads. If a right-tailed test is considered, the p-value of this result is the chance of a fair coin landing on heads at least 14 times out of 20 flips. That probability can be computed from binomial coefficients as


In the above example:

  • Null hypothesis (H0): The coin is fair, with Prob(heads) = 0.5
  • Test statistic: Number of heads
  • Level of significance: 0.05
  • Observation O: 14 heads out of 20 flips; and

Note that the Prob(no. of heads ≤ 14 heads) = 1 - Prob(no. of heads ≥  14 heads) + Prob(no. of head = 14) = 1 - 0.058 + 0.036 = 0.978; however, symmetry of the binomial distribution makes that an unnecessary computation to find the smaller of the two probabilities. Here, the calculated p-value exceeds 0.05, so the observation is consistent with the null hypothesis, as it falls within the range of what would happen 95% of the time were the coin in fact fair. Hence, the null hypothesis at the 5% level is not rejected. Although the coin did not fall evenly, the deviation from expected outcome is small enough to be consistent with chance.

However, had one more head been obtained, the resulting p-value (two-tailed) would have been 0.0414 (4.14%). The null hypothesis is rejected when a 5% cut-off is used.


When the null hypothesis is true, the probability distribution of the p-value is uniform on the interval [0,1]. By contrast, if the alternative hypothesis is true, the distribution is dependent on sample size and the true value of the parameter being studied.[2][12]

The distribution of p-values for a group of studies is called a p-curve.[13] The curve is affected by four factors: the probability that a study is examining a true hypothesis rather than a false hypothesis, the power of the studies investigating true hypotheses, the Type 1 error rates, and publication bias.[14] A p-curve can be used to assess the reliability of scientific literature, such as by detecting publication bias or p-hacking.[13][15]


Pierre-Simon Laplace
Biologist and statistician Ronald Fisher

Computations of p-values date back to the 1770s, where they were calculated by Pierre-Simon Laplace:[16]


The p-value was first formally introduced by Karl Pearson, in his Pearson's chi-squared test,[17] using the chi-squared distribution and notated as capital P.[17] The p-values for the chi-squared distribution (for various values of χ2 and degrees of freedom), now notated as P, was calculated in (Elderton 1902), collected in (Pearson 1914, pp. xxxi–xxxiii, 26–28, Table XII).

The use of the p-value in statistics was popularized by Ronald Fisher,[18] and it plays a central role in his approach to the subject.[19] In his influential book Statistical Methods for Research Workers (1925), Fisher proposes the level p = 0.05, or a 1 in 20 chance of being exceeded by chance, as a limit for statistical significance, and applies this to a normal distribution (as a two-tailed test), thus yielding the rule of two standard deviations (on a normal distribution) for statistical significance (see 68–95–99.7 rule).[20][c][21]


As an illustration of the application of p-values to the design and interpretation of experiments, in his following book The Design of Experiments (1935), Fisher presented the lady tasting tea experiment,[23] which is the archetypal example of the p-value.


Fisher reiterated the p = 0.05 threshold and explained its rationale, stating:[24]


He also applies this threshold to the design of experiments, noting that had only 6 cups been presented (3 of each), a perfect classification would have only yielded a p-value of which would not have met this level of significance.[24] Fisher also underlined the frequentist interpretation of p, as the long-run proportion of values at least as extreme as the data, assuming the null hypothesis is true.




The data obtained by comparing the p-value to a significance level will yield one of two results: either the null hypothesis is rejected, or the null hypothesis cannot be rejected at that significance level (which however does not imply that the null hypothesis is true). In Fisher's formulation, there is a disjunction: a low p-value means either that the null hypothesis is true and a highly improbable event has occurred or that the null hypothesis is false. However, people interpret the p-value in many incorrect ways.

The p-value does not in itself allow reasoning about the probabilities of hypotheses, which requires multiple hypotheses or a range of hypotheses, with a prior distribution of likelihoods between them, in which case Bayesian statistics could be used. There, one uses a likelihood function for all possible values of the prior instead of the p-value for a single null hypothesis.

The p-value refers only to a single hypothesis, called the null hypothesis and does not make reference to or allow conclusions about any other hypotheses, such as the alternative hypothesis in Neyman–Pearson statistical hypothesis testing. In that approach, one instead has a decision function between two alternatives, often based on a test statistic, and computes the rate of type I and type II errors as α and β. However, the p-value of a test statistic cannot be directly compared to these error rates α and β. Instead, it is fed into a decision function.


Critics of p-values point out that the criterion used to decide "statistical significance" is based on an arbitrary choice of alpha level (often set at 0.05), and that this criterion leads to an alarming number of false positive tests. The fraction of all “statistically significant” tests in which the null hypothesis is true may be considerably higher than the alpha level, depending on how many of the null hypotheses were false and how much statistical power was used to test them.[26][27][28]

Dividing results into significant and non-significant effects can be highly misleading.[10][10][29] For instance, analysis of nearly identical datasets can result in p-values that differ greatly in significance.[29] In medical research, p-values were a considerable improvement over previous approaches, but misunderstandings of p-values have become more important for reasons such as the increased statistical complexity of published research.[10] It has been suggested that in fields such as psychology, where studies typically have low statistical power, using significance testing can lead to increased error rates.[29][30]

The use of significance testing as the basis for decisions has also been criticized because of the widespread misunderstandings about the process.[10][31][32] For example, p-values do not address the probability of the null hypothesis being true or false, and the choice of significance threshold should not be arbitrary but instead informed by the consequences of a false positive.[29] It is possible to use Bayes factors for calibration, which allows the use of p-values while reducing the impact of the p-value fallacy, although these approaches introduce other biases as well.[33]

The p-value is incompatible with the likelihood principle and depends on the experiment design, the test statistic in question. That is, the definition of "more extreme" data depends on the sampling methodology adopted by the investigator;[34] for example, the situation in which the investigator flips the coin 100 times, yielding 50 heads, has a set of extreme data that is different from the situation in which the investigator continues to flip the coin until 50 heads are achieved yielding 100 flips.[35]

The incompatibility of the p-value with the likelihood principle has been said by some to indicate a lack of conceptual integrity in this methodology since the intentions of the analyst to analyze the data along differing prescriptions (experimental designs, choice of statistic) yield different results for the same data concerning the same underlying question about the coin's fairness. This concern over the issue of consistent reasoning concerning probable inference led Richard Cox to develop an axiomatic basis for probability conditioned on an essential consistency requirement that leads ultimately to Bayes Rule.[36]

Related quantities[edit]

A closely related concept is the E-value,[37] which is the expected number of times in multiple testing that one expects to obtain a test statistic at least as extreme as the one that was actually observed if one assumes that the null hypothesis is true. The E-value is the product of the number of tests and the p-value.

Orthographic note[edit]

Depending on which style guide is applied, the "p" is styled either italic or not, either capitalized or not, and either hyphenated or not. For example, AMA style uses "P value," APA style uses "p value," and the American Statistical Association uses "p-value."[38]

See also[edit]


  1. ^ Note that the statistical significance of a result does not imply that the result is scientifically significant as well.
  2. ^ It should be noted that a statistical hypothesis is conceptually different from a scientific hypothesis.


  1. ^ Wasserstein, Ronald L.; Lazar, Nicole A. (7 March 2016). "The ASA's Statement on p-Values: Context, Process, and Purpose". The American Statistician. 70 (2): 129–133. doi:10.1080/00031305.2016.1154108. Retrieved 30 October 2016. 
  2. ^ a b Bhattacharya, Bhaskar; Habtzghi, DeSale (2002). "Median of the p value under the alternative hypothesis". The American Statistician. American Statistical Association. 56 (3): 202–6. doi:10.1198/000313002146. Retrieved 19 February 2016. 
  3. ^ Wetzels, R.; Matzke, D.; Lee, M. D.; Rouder, J. N.; Iverson, G. J.; Wagenmakers, E. -J. (2011). "Statistical Evidence in Experimental Psychology: An Empirical Comparison Using 855 t Tests". Perspectives on Psychological Science. 6 (3): 291–298. doi:10.1177/1745691611406923. 
  4. ^ Babbie, E. (2007). The practice of social research 11th ed. Thomson Wadsworth: Belmont, California.
  5. ^ Biau 2010.
  7. ^ Nuzzo, R. (2014). "Scientific method: Statistical errors". Nature. 506 (7487): 150–152. doi:10.1038/506150a. 
  8. ^ a b Wasserstein, Ronald L.; Lazar, Nicole A. (2016). "The ASA's statement on p-values: context, process, and purpose". The American Statistician. doi:10.1080/00031305.2016.1154108. 
  9. ^ "Scientists Perturbed by Loss of Stat Tool to Sift Research Fudge from Fact". Scientific American. April 16, 2015. 
  10. ^ a b c d e Goodman SN (1999). "Toward evidence-based medical statistics. 1: The P value fallacy.". Annals of Internal Medicine. 130 (12): 995–1004. doi:10.7326/0003-4819-130-12-199906150-00008. PMID 10383371. 
  11. ^ Aschwanden, Christie (Mar 7, 2016). "Statisticians Found One Thing They Can Agree On: It's Time To Stop Misusing P-Values". FiveThirtyEight. 
  12. ^ Hung, H.M.J.; O'Neill, R.T.; Bauer, P.; Kohne, K. (1997). "The behavior of the p-value when the alternative hypothesis is true". Biometrics. International Biometric Society. 53 (1): 11–22. doi:10.2307/2533093. JSTOR 2533093. 
  13. ^ a b Head ML, Holman L, Lanfear R, Kahn AT, Jennions MD (2015). "The extent and consequences of p-hacking in science.". PLoS Biol. 13 (3): e1002106. doi:10.1371/journal.pbio.1002106. PMC 4359000Freely accessible. PMID 25768323. 
  14. ^ Lakens D (2015). "What p-hacking really looks like: a comment on Masicampo and LaLande (2012).". Q J Exp Psychol (Hove). 68 (4): 829–32. doi:10.1080/17470218.2014.982664. PMID 25484109. 
  15. ^ Simonsohn U, Nelson LD, Simmons JP (2014). "p-Curve and Effect Size: Correcting for Publication Bias Using Only Significant Results.". Perspect Psychol Sci. 9 (6): 666–81. doi:10.1177/1745691614553988. PMID 26186117. 
  16. ^ Stigler 1986, p. 134.
  17. ^ a b Pearson 1900.
  18. ^ Inman 2004.
  19. ^ Hubbard & Bayarri 2003, p. 1.
  20. ^ Fisher 1925, p. 47, Chapter III. Distributions.
  21. ^ a b Dallal 2012, Note 31: Why P=0.05?.
  24. ^ a b Fisher 1971, Section 7. The Test of Significance.
  25. ^ Fisher 1971, Section 12.1 Scientific Inference and Acceptance Procedures.
  26. ^ Sellke, Thomas; Bayarri, M. J.; Berger, James O. (2001). "Calibration of p Values for Testing Precise Null Hypotheses". The American Statistician . 55 (1): 62–71. doi:10.1198/000313001300339950. JSTOR 2685531. 
  27. ^ Johnson, Valen (2013). "Revised standards for statistical evidence". Proceedings of the National Academy of Sciences USA. 110: 19313–19317. doi:10.1073/pnas.1313476110. 
  28. ^ Colquhoun, David (2015). "An investigation of the false discovery rate and the misinterpretation of p-values". Royal Society Open Science. 1: 140216. doi:10.1098/rsos.140216. 
  29. ^ a b c d Dixon P (2003). "The p-value fallacy and how to avoid it.". Canadian Journal of Experimental Psychology. 57 (3): 189–202. doi:10.1037/h0087425. PMID 14596477. 
  30. ^ Hunter JE (1997). "Needed: A Ban on the Significance Test". Psychological Science. 8 (1): 3–7. doi:10.1111/j.1467-9280.1997.tb00534.x. 
  31. ^ Sterne JA, Smith GD (2001). "Sifting the evidence–what's wrong with significance tests?". BMJ. 322 (7280): 226–231. doi:10.1136/bmj.322.7280.226. PMC 1119478Freely accessible. PMID 11159626. 
  32. ^ Schervish MJ (1996). "P Values: What They Are and What They Are Not". The American Statistician. 50 (3): 203. doi:10.2307/2684655. JSTOR 2684655. 
  33. ^ Sellke T, Bayarri M, Berger JO (2001). "Calibration of p values for testing precise null hypotheses". The American Statistician. 55 (1): 62–71. doi:10.1198/000313001300339950. 
  34. ^ Casson, R. J. (2011). "The pesty P value". Clinical & Experimental Ophthalmology. 39 (9): 849–850. doi:10.1111/j.1442-9071.2011.02707.x. 
  35. ^ Johnson, D. H. (1999). "The Insignificance of Statistical Significance Testing". Journal of Wildlife Management. 63 (3): 763–772. doi:10.2307/3802789. 
  36. ^ Cox, Richard (1961). the Algebra of Probable Inference. Baltimore, MD: The Johns Hopkins Press. ISBN 080186982X. 
  37. ^ National Institutes of Health definition of E-value
  38. ^ http://magazine.amstat.org/wp-content/uploads/STATTKadmin/style[1].pdf

Further reading[edit]

External links[edit]