Estimation statistics

Estimation statistics, or simply estimation, is a data analysis framework that uses a combination of effect sizes, confidence intervals, precision planning, and meta-analysis to plan experiments, analyze data and interpret results. It complements hypothesis testing approaches such as null hypothesis significance testing (NHST), by going beyond the question is an effect present or not, and provides information about how large an effect is. Estimation statistics is sometimes referred to as the new statistics. The primary aim of estimation methods is to report an effect size (a point estimate) along with its confidence interval, the latter of which is related to the precision of the estimate. The confidence interval summarizes a range of likely values of the underlying population effect. Proponents of estimation see reporting a P value as an unhelpful distraction from the important business of reporting an effect size with its confidence intervals, and believe that estimation should replace significance testing for data analysis. Starting in 1929, physicist Raymond Thayer Birge published review papers in which he used weighted-averages methods to calculate estimates of physical constants, a procedure that can be seen as the precursor to modern meta-analysis. In the 1960s, estimation statistics was adopted by the non-physical sciences with the development of the standardized effect size by Jacob Cohen. In the 1970s, modern research synthesis was pioneered by Gene V. Glass with the first systematic review and meta-analysis for psychotherapy. This pioneering work subsequently influenced the adoption of meta-analyses for medical treatments more generally. In the 1980s and 1990s, estimation methods were extended and refined by biostatisticians including Larry Hedges, Michael Borenstein, Doug Altman, Martin Gardner, and many others, with the development of the modern (medical) meta-analysis. Starting in the 1980s, the systematic review, used in conjunction with meta-analysis, became a technique widely used in medical research.