Hatásméretek

Hatásméretek, often translated as effect sizes, are quantitative measures that indicate the magnitude of a phenomenon or the difference between groups. In statistics, they go beyond simply stating whether a result is statistically significant, providing information about the practical importance or strength of an observed effect. Unlike p-values, which assess the probability of obtaining observed data if a null hypothesis were true, hatásméretek estimate the size of the effect itself.

Common types of hatásméretek include Cohen's d, which measures the difference between two means in standard deviation units, and correlation coefficients like Pearson's r, which indicate the strength and direction of a linear relationship between two variables. Other examples include R-squared in regression analysis, representing the proportion of variance in the dependent variable explained by the independent variables, and odds ratios or risk ratios in epidemiological studies.

The interpretation of hatásméretek depends on the context and the specific measure used. General guidelines exist, such as those proposed by Cohen for Cohen's d (e.g., 0.2 for small, 0.5 for medium, and 0.8 for large effects), but these should be applied cautiously. Understanding hatásméretek is crucial for research synthesis, meta-analysis, and for making informed decisions based on research findings, as they help in assessing the real-world impact and generalizability of results. They provide a standardized way to compare findings across different studies, even when sample sizes or measurement units vary.