Mean and Mean difference are the two key statistical measures used in the statistical analysis. Both are essential for meta-analysis as well. Mean and Mean difference are used for the interpretation of a large set of values into a single number which explains the heterogeneity and variation among the individual values. However, one of a common challenge in meta-analysis is the unavailability of this data (mean and standard deviation).
Q & A Forum
Meta Analysis
Q: What are common misinterpretations in P values?
Misinterpretation 1: A higher P value means the intervention does not have effect.
- A P value higher than 0.05 does not prove that an intervention has no observed effect.
- It means that the evidence exist is not sufficient to confirm that an intervention has effect.
- To avoid this, the author should always consider effect size and 95% confidence interval along with P value.
Misinterpretation 1: A higher P value means the intervention does not have effect.
- A lower P value only signifies that the effect is not likely to be exactly zero, but it does not give the measure of clinical importance of the effect.
- In larger meta-analyses, low P values simply determine very less effects that are statistically significant but too small to be meaningful for patients.
- Reviewing the point estimate and confidence interval helps to establish whether the effect size is clinically meaningful.
References:
- Cochrane Handbook for Systematic Reviews of Interventions. (2011). 4.2 P values and statistical significance. The Cochrane Collaboration. Retrieved from https://handbook-5-1.cochrane.org/chapter_12/12_4_2_p_values_and_statistical_significance.htm