Statistical significance

Definition

Statistical significance is a determination that the results of a study or experiment are unlikely to have occurred under the null hypothesis, which assumes no effect or no difference. This concept is crucial for hypothesis testing, as it helps researchers decide whether to reject or fail to reject the null hypothesis. When results are deemed statistically significant, it indicates a strong likelihood that the observed effect is real and not due to random chance.

5 Must Know Facts For Your Next Test

1. Statistical significance typically uses a threshold known as alpha (α), commonly set at 0.05, meaning there is a 5% risk of concluding that an effect exists when there is none.
2. If the p-value is less than or equal to alpha, researchers consider the results statistically significant and may reject the null hypothesis.
3. Statistical significance does not measure the size of an effect or its practical importance; it only indicates whether an effect exists.
4. A result can be statistically significant but not practically significant if the effect size is too small to be meaningful in real-world terms.
5. Confidence intervals can provide additional information on statistical significance by showing the range of values that likely contains the true population parameter.

Review Questions

• How do you determine whether results are statistically significant based on p-values and alpha levels?
  • To determine if results are statistically significant, you compare the p-value obtained from your statistical test to a predetermined alpha level, typically set at 0.05. If the p-value is less than or equal to this alpha level, you conclude that the results are statistically significant, meaning there is strong evidence against the null hypothesis. If the p-value exceeds alpha, you fail to reject the null hypothesis and suggest that any observed effects may be due to chance.
• Discuss how statistical significance differs from practical significance and why both concepts are important in research.
  • Statistical significance indicates that an observed effect is unlikely to have occurred by chance, based on a specific threshold (p-value). However, practical significance assesses whether this effect is meaningful or important in real-world applications. A finding can be statistically significant but have a trivial effect size, making it less relevant in practice. Researchers should consider both concepts to ensure their findings are both reliable and applicable in real-life situations.
• Evaluate how confidence intervals relate to statistical significance and their role in interpreting research results.
  • Confidence intervals provide a range of values that estimate where the true population parameter likely falls, offering insight into both precision and uncertainty of an estimate. They are related to statistical significance because if a confidence interval does not include the null value (usually zero), it indicates that the result is statistically significant. This relationship helps researchers interpret their findings more comprehensively by illustrating not just whether an effect exists, but also the potential range of its magnitude and reliability in research conclusions.

Related terms

p-value: A p-value is the probability of obtaining test results at least as extreme as the observed results, assuming that the null hypothesis is true.

null hypothesis: The null hypothesis is a statement that there is no effect or no difference, which researchers aim to test against in their studies.

confidence interval: A confidence interval is a range of values derived from sample statistics that likely contains the true population parameter with a specified level of confidence.