Understanding Statistical and Practical Significance in Research

When analyzing data in research, the terms statistical significance and practical significance or substantive importance often come into play. Understanding the difference between these two types of significance is crucial for interpreting the results correctly and drawing meaningful conclusions.

Statistical Significance

Statistical significance is a measure of the probability that the observed results did not occur by chance.

In many scholarly studies, a result is considered statistically significant if the p-value is less than the pre-specified alpha level, typically set at 0.05. Here's a simple way to understand this concept: a p-value less than alpha is a statistically significant result, whereas a p-value greater than or equal to alpha is not a statistically significant result. For instance, if the null hypothesis (H0) is that there is no effect or difference, and the p-value is 0.03, then the result is statistically significant because the p-value is less than 0.05. However, if the p-value is 0.06, the result is non-significant, indicating that the observed differences could be due to chance.

The Importance of Thresholds

It's important to note that the threshold (alpha level) is pre-specified before conducting the analysis and should not be adjusted based on the results obtained. This helps prevent the file drawer effect, where significant results are more likely to be published than non-significant ones.

Implications of Non-Significant Results

A non-significant or null result (p > alpha) suggests that the observed data is consistent with the null hypothesis. In such cases, it's often important to consider whether the study was underpowered, meaning it lacked sufficient sample size to detect a true effect, or whether there were other methodological issues. Non-significant results are not necessarily bad; rather, they can guide future research by indicating the need for adjustments in design or methodology.

Practical Significance or Substantive Importance

Practical significance or substantive importance refers to the real-world impact of the findings. This type of significance is often more subjective and relies on the context of the research and the broader implications of the results on the field.

In scholarly papers, a finding is considered significant for the field if it changes the way researchers approach problems or opens up new avenues for research. For example, a new treatment that slightly improves patient outcomes might be considered practically significant even if the statistical significance is borderline, because it offers a new and potentially better option for practitioners. The author may assert practical significance based on an understanding of the potential impact of the finding, but this assertion must be supported and validated by other researchers and the general readership.

Interpreting Non-Significant Findings

Non-significant findings can also be meaningful. For example, they may suggest that the research design needs improvement or that further studies are needed to confirm the results. When discussing non-significant results, the author can highlight what was learned from the study and suggest modifications for future research. This approach ensures that the findings remain valuable, even if they do not meet the traditional criteria of statistical significance.

Conclusion

In summary, understanding both statistical and practical significance is crucial for fully interpreting research findings. While statistical significance provides a measure of the likelihood that the results are due to chance, practical significance helps determine the real-world impact and applicability of the findings.

Key Takeaways:

Statistical significance: p-value Practical significance: the real-world impact of the findings. Non-significant results: may indicate underpowered studies or require further investigation.

By considering both types of significance, researchers can better communicate the full value of their work, even in the face of non-significant results.