inversevariansvektning

Inverse variance weighting is a statistical method used to combine estimates from multiple studies or sources. The core idea is to give more weight to studies that have a smaller variance (i.e., are more precise) and less weight to studies with larger variances (i.e., are less precise). This is based on the principle that a more precise estimate is likely to be closer to the true value.

The weight assigned to each study is inversely proportional to its variance. Mathematically, if a study has an estimated effect size $E$ and a variance $V$, its weight $w$ is calculated as $w = 1/V$. When combining multiple estimates, the overall weighted average is calculated by summing the product of each study's estimate and its weight, and then dividing by the sum of all weights. This ensures that studies with higher precision contribute more significantly to the final pooled estimate.

This method is commonly employed in meta-analysis, a statistical technique used to synthesize results from independent studies. By using inverse variance weighting, meta-analysis aims to produce a more reliable and robust estimate of the overall effect than any single study could provide. It is particularly useful when studies have different sample sizes or different levels of precision in their measurements. The assumption is that the variances are known or can be reliably estimated.