# P-value

This post is part of 'Statistics | General' series

In statistical hypothesis testing, the p-value is the probability of obtaining a test statistic at least as extreme as the one that was actually observed, given that the null hypothesis is true. The null hypothesis is a statement that assumes no significant difference exists between the groups being compared.

For example, if a researcher is testing the effectiveness of a new medication, the null hypothesis might be that the medication has no effect on the condition being treated. The researcher would then perform a statistical test to see if the data provide evidence to reject the null hypothesis in favor of the alternative hypothesis, which is that the medication does have an effect on the condition.

The p-value is used to help determine whether the observed data are statistically significant, or unlikely to have occurred by chance alone, and thus provide evidence to reject the null hypothesis. If the p-value is below a predetermined level of significance (usually 0.05), it suggests that the observed data are unlikely to have occurred by chance and provides evidence to reject the null hypothesis.

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