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P-values are a statistical measure used to determine the significance of results in hypothesis testing. They represent the probability of obtaining results at least as extreme as the observed data, assuming that the null hypothesis is true. A low p-value (typically ≤ 0.05) indicates strong evidence against the null hypothesis, suggesting that the observed data is unlikely to have occurred by chance. Conversely, a high p-value suggests that the observed data is consistent with the null hypothesis.

P-values are widely used in various fields, including medicine, psychology, and social sciences, to assess the validity of research findings. However, they have been subject to criticism and debate in recent years. Some statisticians argue that p-values alone are insufficient for making decisions about the truth of a hypothesis, as they do not provide information about the size or direction of an effect. Additionally, the use of p-values has been criticized for promoting a focus on significance testing rather than effect sizes and confidence intervals.

Despite these criticisms, p-values remain an essential tool in statistical analysis. They provide a standardized way to evaluate the strength of evidence against a null hypothesis and are widely accepted and understood in the scientific community. However, it is crucial to interpret p-values in the context of the overall research design, sample size, and effect size.