Statistical significance

Definition

Statistical significance is a mathematical measure that helps determine whether the results of a study are likely due to chance or reflect a true effect. It is typically assessed using a p-value, which quantifies the probability of observing the results if the null hypothesis were true. A low p-value indicates that the observed effects are unlikely to have occurred by random chance, suggesting that they are statistically significant and worth further investigation.

5 Must Know Facts For Your Next Test

1. A commonly used threshold for statistical significance is a p-value of less than 0.05, meaning there is less than a 5% probability that the observed results occurred by chance.
2. Statistical significance does not imply practical significance; findings can be statistically significant yet have minimal real-world impact.
3. When researchers report statistical significance, they often use it to reject the null hypothesis, indicating that there is evidence to support an effect or difference.
4. Misinterpretation of statistical significance can lead to overestimating the reliability of findings, especially when sample sizes are small or results are manipulated.
5. In environmental health studies, statistical significance is crucial for determining the relationship between exposure to environmental factors and health outcomes.

Review Questions

• How does understanding statistical significance enhance the interpretation of research findings in environmental health?
  • Understanding statistical significance is vital in interpreting research findings because it helps identify whether observed effects are likely genuine or simply due to chance. In environmental health, this means distinguishing between real associations between environmental exposures and health outcomes versus random fluctuations in data. This understanding aids public health professionals in making informed decisions based on reliable evidence.
• Discuss how a low p-value contributes to the assessment of environmental health risks associated with exposure to pollutants.
  • A low p-value suggests that the likelihood of observing such results under the null hypothesis is minimal, which can indicate a significant relationship between exposure to pollutants and adverse health outcomes. This assessment allows researchers and policymakers to recognize and prioritize risks associated with certain environmental factors. Ultimately, understanding these risks can lead to better regulatory actions and public health interventions aimed at reducing exposure and protecting community health.
• Evaluate the implications of relying solely on statistical significance when making public health recommendations based on environmental health studies.
  • Relying solely on statistical significance can be misleading when making public health recommendations because it may overlook important factors such as effect size and real-world applicability. A finding may be statistically significant but not practically significant; thus, focusing only on p-values might lead to recommendations that don't address actual risks effectively. It's essential to combine statistical analysis with context, including consideration of confidence intervals and other metrics, to ensure that public health guidelines are both scientifically valid and relevant to community needs.

Related terms

p-value: A p-value is a statistical measure that indicates the probability of obtaining results at least as extreme as the observed results, under the assumption that the null hypothesis is true.

null hypothesis: The null hypothesis is a statement in statistical testing that assumes no effect or no difference exists between groups in an experiment.

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