Search results
Results from the WOW.Com Content Network
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 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, [7] and believe that estimation should replace significance testing for data analysis ...
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.
Maimonides' rule is named after the 12th-century rabbinic scholar Maimonides, who identified a correlation between class size and students' achievements. [1] Today this rule is widely used in educational research to evaluate the effect of class size on students' test scores.
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]
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
In other words, the correlation is the difference between the common language effect size and its complement. For example, if the common language effect size is 60%, then the rank-biserial r equals 60% minus 40%, or r = 0.20. The Kerby formula is directional, with positive values indicating that the results support the hypothesis.
The text is: "So, in the example above of visiting England and observing men's and women's heights, the data (Aaron,Kromrey,& Ferron, 1998, November; from a 2004 UK representative sample of 2436 men and 3311 women) are: Men: mean height = 1750 mm; standard deviation = 89.93 mm Women: mean height = 1612 mm; standard deviation = 69.05 mm The ...