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P-value

P-value

P-value is one of the most crucial concepts in statistics.
P-value stands for the probability value. P-value is the probability that, if the null hypothesis is true, the results from another randomly selected sample will be as extreme or more extreme as the results obtained from the given sample.
A large P-value calculated from the data indicates to not reject the null hypothesis. But a higher P-value does not mean that the null hypothesis is true. The smaller the P-value, the more...

Decision Making: P-value Method

Decision Making: P-value Method

The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim is also stated. These statements can act as null and alternative hypotheses: a null hypothesis would be a neutral statement while the alternative hypothesis can...

Statistical Significance

Statistical Significance

Once data is collected from both the experimental and the control groups, a statistical analysis is conducted to find out if there are meaningful differences between the two groups. A statistical analysis determines how likely any difference found is due to chance (and thus not meaningful). In psychology, group differences are considered meaningful, or significant, if the odds that these differences occurred by chance alone are 5 percent or less. Stated another way, if we repeated this...

Testing a Claim about Population Proportion

Testing a Claim about Population Proportion

A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...

Decision Making: Traditional Method

Decision Making: Traditional Method

The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...

Bonferroni Test

Bonferroni Test

The Bonferroni test is a statistical test named after Carlo Emilio Bonferroni, an Italian mathematician best known for Bonferroni inequalities. This statistical test is a type of multiple comparison test to determine which means are different than the rest. Bonferroni test can minimize the Type 1 error by reducing the significance level alpha, which otherwise increases with sample pairs.
The means of different samples are first paired in all possible combinations.
The null hypothesis of the...

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Related Experiment Video

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Mapping Dysfunctional Protein-Protein Interactions in Disease

Mapping Dysfunctional Protein-Protein Interactions in Disease

Published on: October 24, 2025

P-Value Demystified.

Amrita Sil¹, Jayadev Betkerur², Nilay Kanti Das³

¹Department of Pharmacology, Rampurhat Government Medical College, Rampurhat, Birbhum, West Bengal, India.

Indian Dermatology Online Journal

|March 21, 2020

Summary

This summary is machine-generated.

The P value, a key part of null hypothesis significance testing, indicates the probability of results occurring by chance. While useful, researchers should also report effect size and confidence intervals for robust findings.

Keywords:

Confidence interval P value hypothesis testing non-parametric data null hypothesis null hypothesis significance testing parametric data

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Area of Science:

Biostatistics
Scientific Methodology

Background:

The P value is central to null hypothesis significance testing in biomedical research.
It represents the probability of observing data by chance if the null hypothesis is true.
Significance is typically achieved when the P value is less than 5%.

Purpose of the Study:

To discuss the role and limitations of the P value in scientific research.
To highlight the importance of effect size, confidence intervals, and descriptive statistics.
To guide the selection of appropriate statistical tests for assessing significance.

Main Methods:

Discussion of the interpretation and application of P values.
Emphasis on the influence of sample size on P value outcomes.
Consideration of alternative statistical measures beyond the P value.

Main Results:

P values offer a measure of evidence against a null hypothesis.
Large sample sizes can lead to statistically significant P values, irrespective of practical importance.
Over-reliance on P values may hinder research generalizability and reproducibility.

Conclusions:

While criticized, P values remain important for precise estimation in null hypothesis significance testing.
Researchers should supplement P values with effect sizes, confidence intervals, and other relevant statistics.
Choosing the correct statistical test based on data type, pairing, and group number is crucial for accurate significance assessment.