P-values are crucial in hypothesis testing, quantifying the likelihood of observing results as extreme as the data, assuming the null hypothesis is true. They help decide whether to reject or fail to reject the null hypothesis based on the strength of evidence against it.

Interpreting p-values involves comparing them to predetermined significance levels. Smaller p-values indicate stronger evidence against the null hypothesis, while larger ones suggest consistency with it. However, p-values have limitations, including potential overinterpretation and lack of effect size information.

Understanding P-values and Significance Levels

P-values in hypothesis testing

• P-value quantifies probability of observing results as extreme or more extreme than actual data, assuming null hypothesis true
• Plays crucial role in hypothesis testing by providing evidence against null hypothesis (H₀)
• Used to make decisions about rejecting or failing to reject H₀ based on strength of evidence
• Calculated using test statistic and sampling distribution, varies depending on specific statistical test (t-test, chi-square test)

Interpretation of p-values

• Smaller p-values indicate stronger evidence against H₀ (p = 0.01 stronger than p = 0.05)
• Larger p-values suggest data consistent with H₀ (p = 0.8 more consistent than p = 0.2)
• Decision-making process involves comparing p-value to predetermined significance level (α)
• Reject H₀ if p-value < α, fail to reject if p-value ≥ α
• Common significance levels: α = 0.05, 0.01, 0.10

P-values vs null hypothesis

• Inverse relationship exists between p-values and strength of evidence against H₀
• P-values provide continuous measure of evidence, more informative than binary decisions
• Interpretation guidelines:
  • p < 0.01: Very strong evidence against H₀
  • 0.01 ≤ p < 0.05: Strong evidence against H₀
  • 0.05 ≤ p < 0.10: Weak evidence against H₀
  • p ≥ 0.10: Little to no evidence against H₀

Limitations of p-values

• Arbitrary threshold problem leads to potential overinterpretation of results near significance level
• Lack of effect size information means p-values don't indicate magnitude of effect
• Sample size sensitivity can result in small p-values for trivial effects in large samples
• Multiple testing issues increase risk of Type I errors, requiring correction methods (Bonferroni)
• Misinterpretation risks include confusing p-value with probability of H₀ being true
• Encourages dichotomous thinking, oversimplifying complex research questions
• Publication bias favors statistically significant results, distorting overall understanding