P-value

Definition

A p-value is a statistical measure that helps determine the significance of results obtained from hypothesis testing. It indicates the probability of obtaining test results at least as extreme as the observed results, assuming that the null hypothesis is true. A smaller p-value suggests stronger evidence against the null hypothesis, often leading researchers to consider rejecting it in favor of an alternative hypothesis.

5 Must Know Facts For Your Next Test

1. The p-value ranges from 0 to 1, with lower values indicating stronger evidence against the null hypothesis.
2. A common threshold for statistical significance is 0.05; if the p-value is below this threshold, researchers often reject the null hypothesis.
3. A p-value does not measure the probability that the null hypothesis is true; rather, it assesses how compatible the data are with the null hypothesis.
4. P-values can be influenced by sample size; larger samples may yield smaller p-values even for small effects, making context crucial.
5. Misinterpretation of p-values is common; a p-value alone does not imply practical significance or importance of a result.

Review Questions

• How does a p-value inform decision-making in hypothesis testing?
  • A p-value plays a critical role in decision-making during hypothesis testing by quantifying the strength of evidence against the null hypothesis. When researchers calculate a p-value, they compare it to a pre-set significance level, typically 0.05. If the p-value falls below this threshold, they may reject the null hypothesis, suggesting that their observed data provide strong enough evidence to support an alternative hypothesis.
• Discuss the implications of sample size on p-values and why it is important to consider this when interpreting results.
  • Sample size significantly impacts p-values because larger samples can lead to smaller p-values even when the effect size is minimal. This can create a misleading impression of significance when, in fact, the results may not be practically meaningful. Therefore, it’s essential to interpret p-values in context, considering both sample size and effect size to draw appropriate conclusions from research findings.
• Evaluate how misinterpretations of p-values can affect public health policy decisions.
  • Misinterpretations of p-values can have serious consequences for public health policy decisions by leading policymakers to overestimate the importance or validity of certain findings. For instance, if a study reports a statistically significant result based solely on a low p-value without considering effect size or real-world relevance, policies may be implemented based on flawed evidence. This could result in wasted resources or misguided strategies that do not effectively address public health issues, highlighting the need for critical assessment of research findings beyond just their statistical significance.

Related terms

Null Hypothesis: A statement that there is no effect or no difference, which researchers aim to test against an alternative hypothesis.

Alternative Hypothesis: The hypothesis that there is an effect or a difference, which researchers seek to support by rejecting the null hypothesis.

Statistical Significance: A determination that the observed results are unlikely to have occurred under the null hypothesis, often assessed using a predetermined significance level (like 0.05).