What does it mean when results show statistical significance?

Sourav PanNovember 8, 2024

Answered

Statistical significance indicates that the results of a study or experiment are unlikely to have occurred by chance alone. When a result is deemed statistically significant, it suggests that there is a meaningful relationship or effect present, rather than random variability.

Key Concepts

1. Null Hypothesis: Statistical significance is typically assessed against a null hypothesis, which posits that there is no effect or relationship between the variables being studied. The goal of hypothesis testing is to determine whether the observed data provide enough evidence to reject this null hypothesis.
2. P-Value: The p-value is a crucial metric in determining statistical significance. It represents the probability of observing the data, or something more extreme, if the null hypothesis were true. A common threshold for statistical significance is a p-value of less than 0.05, meaning there is less than a 5% chance that the observed results are due to random variation under the null hypothesis.
3. Significance Level (α): This is the threshold set by researchers before conducting an analysis, often at 0.05 or 0.01. If the p-value obtained from the analysis is less than or equal to this significance level, the result is considered statistically significant.

Implications of Statistical Significance

• Not Proof of Truth: While statistical significance suggests that a result is unlikely due to chance, it does not prove that the hypothesis is true or that there is a practical effect. It merely indicates that further investigation may be warranted.
• Effect Size Matters: Statistical significance does not convey the size or importance of an effect. A statistically significant result can still be trivial if the effect size (the magnitude of the difference or relationship) is small.
• Contextual Interpretation: Researchers must interpret statistical significance within the context of their study. Factors such as sample size, study design, and real-world applicability should be considered when evaluating the findings