Interpretation of p-values in Hypothesis Testing using Statistical Significance Levels
The p-value represents the probability of obtaining test results at least as extreme as the observed data under the assumption that the null hypothesis is true. It serves as a metric to assess whether sample differences are statistically significant or attributable to random sampling error within the domain of inferential statistics and hypothesis testing. Rejecting or failing to reject the null hypothesis depends on comparing this probability against a pre-determined significance level (alpha), typically set at 0.05, which quantifies the acceptable risk of Type I errors in scientific inquiry.
Interpretation of p-values in Hypothesis Testing using Statistical Significance Levels
The p-value represents the probability of obtaining test results at least as extreme as the observed data under the assumption that the null hypothesis is true. It serves as a metric to assess whethe…