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In statistics, an effect size is a value measuring the strength of the relationship between two variables in a population, or a sample-based estimate of that quantity. It can refer to the value of a statistic calculated from a sample of data, the value of one parameter for a hypothetical population, or to the equation that operationalizes how statistics or parameters lead to the effect size ...
The counternull value is the effect size that is just as well supported by the data as the null hypothesis. [2] In particular, when results are drawn from a distribution that is symmetrical about its mean, the counternull value is exactly twice the observed effect size. The null hypothesis is a hypothesis set up to be tested against an alternative.
In the top panel, all observed values are shown. The effect sizes, sampling distribution, and 95% confidence intervals are plotted on a separate axes beneath the raw data. For each group, summary measurements (mean ± standard deviation) are drawn as gapped lines.
Effect of selected alterable variables on student achievement [1]: 6 [10] Object of change process Alterable variable Effect size Percentile equivalent Teacher Tutorial instruction: 2.00 98 Teacher Reinforcement 1.2 Learner Feedback-corrective (mastery learning) 1.00 84 Teacher Cues and explanations 1.00 Teacher, Learner
An effect size can be a direct value of the quantity of interest (for example, a difference in mean of a particular size), or it can be a standardized measure that also accounts for the variability in the population (such as a difference in means expressed as a multiple of the standard deviation).
It is the mean divided by the standard deviation of a difference between two random values each from one of two groups. It was initially proposed for quality control [1] and hit selection [2] in high-throughput screening (HTS) and has become a statistical parameter measuring effect sizes for the comparison of any two groups with random values. [3]
It may also be considered a general measure of effect size, quantifying the "magnitude" of the effect of one variable on another. For simple linear regression with orthogonal predictors, the standardized regression coefficient equals the correlation between the independent and dependent variables.
Cohen et al. (2003) recommended using the following to probe the simple effect of gender on the dependent variable (Y) at three levels of the continuous independent variable: high (one standard deviation above the mean), moderate (at the mean), and low (one standard deviation below the mean). [7]