p-value

Probability of
Null Hypothesis being true

If p is less than a threshold , reject

Null Hypothesis.

A p-value is basically the likelihood of finding a mean difference by chance if indeed there is no difference.

The p-value represents the probability of obtaining

Test statistic at least as extreme as the observed results, assuming that the null hypothesis is true.

Key Concepts

Test Statistics and Null Hypothesis

Most test statistics are calculated under the assumption of a null hypothesis
For example, when comparing two sample means, we assume both samples come from the same population

Statistical Significance

Commonly uses a 5% threshold - if p-value < 0.05, we consider the difference significant
Tests whether the differences between sample groups are statistically meaningful

Important Considerations

The p-value incorporates both effect size and sample size (n) information. This means that even samples from the same population might show p-values below 0.05 due to large sample sizes. Therefore, it's crucial not to rely solely on p-values for decision-making.

P-values are specifically related to the

T test and help determine the statistical significance of observed differences between groups.

p-value

In null-hypothesis significance testing, the p-value[note 1] is the probability of obtaining test results at least as extreme as the result actually observed, under the assumption that the null hypothesis is correct.[2][3] A very small p-value means that such an extreme observed outcome would be very unlikely under the null hypothesis. Even though reporting p-values of statistical tests is common practice in academic publications of many quantitative fields, misinterpretation and misuse of p-values is widespread and has been a major topic in mathematics and metascience.[4][5]