P-value

Definition

A p-value is a statistical measure that helps determine the significance of results in hypothesis testing. It indicates the probability of observing the obtained results, or something more extreme, under the assumption that the null hypothesis is true. A low p-value suggests strong evidence against the null hypothesis, while a high p-value indicates weak evidence, thus helping researchers decide whether to reject or fail to reject the null hypothesis.

5 Must Know Facts For Your Next Test

1. A p-value is often compared to a predetermined significance level (like 0.05) to determine if results are statistically significant.
2. P-values can range from 0 to 1; smaller p-values indicate stronger evidence against the null hypothesis.
3. A p-value does not indicate the size or importance of an effect; it only assesses whether an observed effect is statistically significant.
4. It is possible to have statistically significant results (low p-value) that are not practically significant, meaning they may not have real-world relevance.
5. Misinterpretation of p-values can occur, such as assuming that a p-value tells you the probability that the null hypothesis is true.

Review Questions

• How does the p-value assist in determining whether to reject the null hypothesis?
  • The p-value plays a crucial role in hypothesis testing by providing a measure of evidence against the null hypothesis. If the p-value is less than or equal to the predetermined significance level (usually set at 0.05), it suggests that the observed data is unlikely under the null hypothesis. Therefore, researchers would reject the null hypothesis, indicating that there is sufficient evidence to support an alternative hypothesis.
• Discuss how p-values can impact decision-making in public health research.
  • P-values are critical in public health research as they help determine whether findings are statistically significant. A low p-value might suggest that an intervention is effective or that an exposure has a significant impact on health outcomes, influencing policy decisions and resource allocation. However, reliance solely on p-values without considering practical significance and context may lead to misguided conclusions about public health interventions.
• Evaluate the implications of misinterpreting p-values in research studies and their potential consequences.
  • Misinterpretation of p-values can have serious implications in research studies. For example, assuming that a low p-value guarantees clinical significance can lead researchers to promote ineffective treatments or interventions based solely on statistical results. Additionally, this misunderstanding may contribute to issues such as publication bias, where only studies with significant findings are published, ultimately skewing the scientific literature and impacting public health policies based on incomplete information.

Related terms

Null Hypothesis: A statement that there is no effect or no difference, used as a starting point for statistical testing.

Significance Level: A threshold set before a study, commonly denoted as alpha (α), which determines when to reject the null hypothesis, often set at 0.05.

Confidence Interval:

A range of values derived from sample data that is likely to contain the population parameter with a certain level of confidence.