Pitfalls to Avoid When Using P-Values in Hypothesis Testing

Pitfalls to Avoid When Using P-Values in Hypothesis Testing

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Frequently Asked Questions

A P-value indicates the probability of observing results as extreme as, or more extreme than, the results obtained, assuming that the null hypothesis is true. It helps determine the statistical significance of your findings.
P-values are often misinterpreted as the probability that the null hypothesis is true, or as the probability that the alternative hypothesis is false. They only provide evidence against the null hypothesis.
P-hacking refers to manipulating data or analysis methods until a P-value reaches a desired level of significance (typically p < 0.05). This practice inflates the false positive rate and leads to unreliable conclusions.
With a large enough sample size, even trivial effects can produce statistically significant P-values. Conversely, with a small sample size, meaningful effects may not reach statistical significance.
No, P-values should not be the only factor in decision-making. Consider effect size, practical significance, and the context of the research question.
Statistical significance indicates whether an effect is likely due to chance, while practical significance refers to the real-world importance or usefulness of the effect.
Performing multiple statistical tests increases the chance of finding a statistically significant result by chance alone. Adjustments like Bonferroni correction are needed to account for this.
A confidence interval provides a range of plausible values for a population parameter. It complements P-values by providing information about the magnitude and precision of the effect. If the null hypothesis value falls outside the confidence interval, the result is statistically significant.
Pre-registration helps prevent P-hacking by specifying the research questions, hypotheses, and analysis methods before data collection. This increases the transparency and credibility of research findings.