key term - Statistical significance

Definition

Statistical significance refers to the likelihood that a relationship or difference observed in data is not due to random chance, but rather reflects a true effect in the population. It helps researchers determine if their findings are reliable and meaningful by comparing p-values to a predetermined significance level, often set at 0.05. When a result is deemed statistically significant, it suggests that there is enough evidence to reject the null hypothesis in favor of an alternative hypothesis.

5 Must Know Facts For Your Next Test

1. A result is typically considered statistically significant if the p-value is less than or equal to the alpha level, often set at 0.05, indicating a 5% risk of concluding that a difference exists when there is none.
2. In hypothesis testing, statistical significance helps researchers determine if their results support the rejection of the null hypothesis, which states there is no effect or difference.
3. Statistical significance does not imply practical significance; a result can be statistically significant but may not have real-world implications.
4. In regression analysis, statistical significance of the slope indicates whether there is enough evidence to suggest that changes in the predictor variable have an effect on the response variable.
5. When testing for differences between two population means, statistical significance helps determine if any observed differences are likely due to real differences in populations rather than sampling variability.

Review Questions

• How does statistical significance inform researchers about the reliability of their findings?
  • Statistical significance indicates whether the observed effects in data are likely genuine or simply due to random chance. By using a p-value and comparing it to a predetermined alpha level, researchers can assess if they should reject the null hypothesis. A statistically significant result gives researchers confidence that their findings are not just flukes, thus guiding future decisions based on these results.
• Discuss how statistical significance differs from practical significance in the context of research findings.
  • Statistical significance indicates whether an observed effect in data is unlikely to have occurred by random chance. However, practical significance considers whether that effect is meaningful in a real-world context. For example, a drug may show statistically significant improvement over a placebo with p < 0.05, but if the actual improvement in health outcomes is trivial, it may lack practical significance despite being statistically noteworthy.
• Evaluate how statistical significance plays a role in determining the slope of a regression model and its implications for understanding relationships between variables.
  • Statistical significance in the context of a regression model assesses whether the slope coefficient is significantly different from zero, suggesting a meaningful relationship between predictor and response variables. If the slope is statistically significant, it indicates that changes in the predictor variable reliably result in changes in the response variable, thus providing valuable insights into their relationship. Understanding this helps researchers make informed decisions and predictions based on their models, highlighting the importance of statistical significance in analyzing data relationships.

Related terms

P-value:

The probability of obtaining results at least as extreme as the observed results, assuming that the null hypothesis is true.

Null Hypothesis:

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

Confidence Interval:

A range of values that is likely to contain the true population parameter, providing an estimate of uncertainty around a sample statistic.