Language
Country/Area
No result found
Statistical significance vs. practical significance: Why is the difference important?
In today's fields of medicine and psychology, the effects of research and treatments often need to be evaluated in terms of "statistical significance" and "practical significance." However, the difference between the two goes beyond terminology and has profound implications for patient outcomes and mental health. Here, we’ll delve into the definition of both meanings and their importance.
Definition of statistical significance
Statistical significance is mainly used in hypothesis testing, with the purpose of detecting the establishment of the null hypothesis. The null hypothesis usually asserts that there is no relationship between variables. Researchers set a significance level, the most common being α = 0.05 or 0.01, which represents the probability of falsely rejecting the true null hypothesis. Obtaining statistically significant results indicates that the observed results have a very low probability of occurring given that the null hypothesis is true.
However, statistical significance only shows the likelihood that the data are consistent with the null hypothesis and does not represent the clinical significance of the results.
Definition of actual meaning
Practical significance focuses on the true effect of the intervention or treatment. Specifically, whether the treatment brought about enough changes to benefit the patient's daily life. In the context of clinical care, practical significance reveals the importance of a study result and is often measured using metrics such as effect size, number necessary to treat (NNT), or proportion prevented.
Practical implications include not only the magnitude of results, but also emphasis on individual changes, not just group effects.
Clinical Use
In psychology and psychotherapy, clinical significance goes a step further and assesses whether a treatment is effective enough to change a patient's diagnostic label. Specifically, clinical significance answers the question of whether this treatment is sufficient to bring patients back to normal.
Example analysis
For example, a treatment may show statistically significant reductions in depressive symptoms, but may no longer have clinical significance if only 40% of patients no longer meet diagnostic criteria for depression.
Just because a treatment achieves statistically significant and large effect sizes does not mean that it can transform patients from dysfunctional to normal.
Calculation method of clinical significance
There are various methods for calculating clinical significance. Five common methods include:
- Jacobson-Truax method
- Gulliksen-Lord-Novick method
- Edwards-Nunnally method
- Hageman-Arrindell method
- Hierarchical Linear Model (HLM)
Conclusion
Understanding the difference between statistical significance and practical significance is critical to the interpretation of clinical studies and the treatment of patients. Not only will this help us better assess the true effectiveness of the therapy, but it will also provide guidance for the next intervention. Ultimately, such distinctions can inspire the search for and use of effective treatments that can improve patients' quality of life. While thinking about these questions, how do you think we can better evaluate the actual effectiveness of treatments?
