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Statistical power, or sensitivity, is the likelihood of a significance test detecting an effect when there actually is one. A true effect is a real, non-zero relationship between variables in a population. An effect is usually indicated by a real difference between groups or a correlation between variables.
Statistical power, also called sensitivity, indicates the probability that a study can distinguish an actual effect from a chance occurrence. It represents the probability that a test correctly rejects the null hypothesis (i.e., it represents the probability of avoiding a Type I error).
In frequentist statistics, power is a measure of the ability of an experimental design and hypothesis testing setup to detect a particular effect if it is truly present.
Simply put, power is the probability of not making a Type II error, according to Neil Weiss in Introductory Statistics. Mathematically, power is 1 – beta. The power of a hypothesis test is between 0 and 1; if the power is close to 1, the hypothesis test is very good at detecting a false null hypothesis.
Power in statistics is the probability that a hypothesis test can detect an effect in a sample when it exists in the population. It is the sensitivity of a hypothesis test. When an effect exists in the population, how likely is the test to detect it in your sample? You need the power!
The power of a hypothesis test is the probability of making the correct decision if the alternative hypothesis is true. That is, the power of a hypothesis test is the probability of rejecting the null hypothesis \(H_0\) when the alternative hypothesis \(H_A\) is the hypothesis that is true.
In statistics, power is the probability of rejecting a false null hypothesis. Power Calculation Example. Power & Alpha Level. Power & Effect Size. Power & Sample Size. 3 Main Reasons for Power Calculations. Software for Power Calculations - G*Power. Power - Minimal Example. In some country, IQ and salary have a population correlation ρ = 0.10.
What is statistical power, when should it be used, and what information is needed for calculating power? Discussion. Like the p value, the power is a conditional probability. In a hypothesis test, the alternative hypothesis is the statement that the null hypothesis is false.
In other words, statistical power is a decision by a researcher/statistician that results of a study/experiment can be explained by factors other than chance alone. The statistical power of a study is also referred to as its sensitivity in some cases.
Statistical power is a measure of study efficiency, calculated before conducting the study to estimate the chance of discovering a true effect rather than obtaining a false negative result, or worse, overestimating the effect by detecting the noise in the data.