طلبتكم يا خواتي لا تردوني

تكفون بسرعه يا حبايبي
الوقت قاعد يمر

6 مشاهات و ولا رد
الله يسامحكم
تكفوون ساعدوني

The power of a statistical test is the probability that the test will reject the null hypothesis when the alternative hypothesis is true (i.e. that it will not make a Type II error ). As power increases, the chances of a Type II error decrease. The probability of a Type II error is referred to as the false negative rate ( ). Therefore power is equal to 1 .
Power analysis can be used to calculate the minimum sample size required to accept the outcome of a statistical test with a particular level of confidence. It can also be used to calculate the minimum effect size that is likely to be detected in a study using a given sample size. In addition, the concept of power is used to make comparisons between different statistical tests: for example, between a parametric and a nonparametric test of the same hypothesis.
انتي ما كتبتي من اي ناحية نبحث لك لكن اتمنى هذا يفيدك
Statistical tests use data from samples to assess, or make inferences about, a population . In the concrete setting of a two-sample comparison, the goal is to assess whether the mean values of some attribute obtained for individuals in two sub-populations differ. For example, to test the null hypothesis that the mean scores of men and women on a test do not differ, samples of men and women are drawn, the test is administered to them, and the mean score of one group is compared to that of the other group using a statistical test such as the two-sample Z-test. The power of the test is the probability that the test will find a statistically significant difference between men and women, as a function of the size of the true difference between those two populations. Note that power is the probability of finding a difference that does exist, as opposed to the likelihood of declaring a difference that does not exist (which is known as a Type I error).