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Value of p-value in biomedical research.

Panagiotakos DB - Open Cardiovasc Med J (2008)

Bottom Line: Significance tests and the corresponding p-values play a crucial role in decision making.In this commentary the meaning, interpretation and misinterpretation of p-values is presented.Alternatives for evaluating the reported evidence are also discussed.

View Article: PubMed Central - PubMed

Affiliation: Department of Dietetics - Nutrition, Harokopio University, Athens, Greece. d.b.panagiotakos@usa.net

ABSTRACT
Significance tests and the corresponding p-values play a crucial role in decision making. In this commentary the meaning, interpretation and misinterpretation of p-values is presented. Alternatives for evaluating the reported evidence are also discussed.

No MeSH data available.


Theoretical example of p-values in relation to sample size for the same difference in the data.
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Figure 1: Theoretical example of p-values in relation to sample size for the same difference in the data.

Mentions: The p-value is not the probability that the hypothesis is true, and this is because hypotheses do not have probabilities in classical statistics. Moreover, the p-value is not the probability of falsely rejecting the hypothesis. Falsely rejecting the mull hypothesis is a Type I error. This error is a version of the so-called “prosecutor's fallacy”. The Type I error rate is closely related to the p-value since we reject the hypothesis when p-value is less than a pre-defined level, α. The p-value does not indicate the size or importance of the observed effect. Thus, a very small p-value, let say 0.000… (usually presented as <0.001) does not necessarily mean a strong association (compared with effect size which is a measure of the strength of the relationship between 2 variables, e.g. odds ratio, relative risk, correlation coefficient, Cohen’s d etc [5, 6]). Moreover, the p-value is influenced by sample size. For example, the Fig. (1) illustrates the impressive decrease in p-value according to sample size, keeping the observed findings constant. It can be seen that if the initial sample size is doubled (i.e. n=200 per treatment arm) the study’s results achieve significance.


Value of p-value in biomedical research.

Panagiotakos DB - Open Cardiovasc Med J (2008)

Theoretical example of p-values in relation to sample size for the same difference in the data.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2627527&req=5

Figure 1: Theoretical example of p-values in relation to sample size for the same difference in the data.
Mentions: The p-value is not the probability that the hypothesis is true, and this is because hypotheses do not have probabilities in classical statistics. Moreover, the p-value is not the probability of falsely rejecting the hypothesis. Falsely rejecting the mull hypothesis is a Type I error. This error is a version of the so-called “prosecutor's fallacy”. The Type I error rate is closely related to the p-value since we reject the hypothesis when p-value is less than a pre-defined level, α. The p-value does not indicate the size or importance of the observed effect. Thus, a very small p-value, let say 0.000… (usually presented as <0.001) does not necessarily mean a strong association (compared with effect size which is a measure of the strength of the relationship between 2 variables, e.g. odds ratio, relative risk, correlation coefficient, Cohen’s d etc [5, 6]). Moreover, the p-value is influenced by sample size. For example, the Fig. (1) illustrates the impressive decrease in p-value according to sample size, keeping the observed findings constant. It can be seen that if the initial sample size is doubled (i.e. n=200 per treatment arm) the study’s results achieve significance.

Bottom Line: Significance tests and the corresponding p-values play a crucial role in decision making.In this commentary the meaning, interpretation and misinterpretation of p-values is presented.Alternatives for evaluating the reported evidence are also discussed.

View Article: PubMed Central - PubMed

Affiliation: Department of Dietetics - Nutrition, Harokopio University, Athens, Greece. d.b.panagiotakos@usa.net

ABSTRACT
Significance tests and the corresponding p-values play a crucial role in decision making. In this commentary the meaning, interpretation and misinterpretation of p-values is presented. Alternatives for evaluating the reported evidence are also discussed.

No MeSH data available.