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The ability of a statistical test to verify the falsity of the null hypothesis.  Power is expressed as 1 - , where is the probability of a Type II Error.  In general, the specific value of ß is unknown, but it is affected by many aspects of the experiment as well as by the statistical procedures chosen by the researcher.  Obviously, having a powerful experiment is desirable.
The probability that a study can distinguish between a true exposure-to-disease relationship and a coincidence. The power of a study depends on the size of a study population, the amount of radiation exposure and the number of cases of the disease under investigation.
The power of a hypothesis test, (at a specified level of significance) is the probability of rejecting H0 when H0 is actually false, i.e. of the test correctly rejecting the null hypothesis. It is almost always a function of the parameter(s) that vary under H1.
The probability of correctly rejecting a false H0 .
The ability of a study to detect a statistically significant result, usually calculated before a study is conducted to determine the necessary sample size.
The ability to reject the null hypothesis when it is false.
the probability that a test will reject the null hypothesis when it is, in fact, false.
The probability that a statistical procedure or research design will detect differences or effects when they are present. Researchers use power to determine how likely they are to find "true," significant results based on the size of the sample.
The probability of finding a true difference or association when one actually exists. Statistical power is defined as 1.0 â€“ Î². Beta is Type II error, the probability of failing to find an actual difference or association, so the probability of finding real effects in a test is the probability that one exists minus the probability of missing it. [See alpha, operating characteristic curve, power curve, Type II error
The chance that an experimental study will correctly observe a statistically significant difference between the study groups. This may be considered the "sensitivity" of the study trial itself for detecting a difference when it is there.(empty)
The probability of rejecting a false null hypothesis.
In hypothesis testing, the power refers to the probability of making a correct decision to reject the null hypothesis. Power tells us the likelihood of detecting a difference between groups, or a hypothesized relationship within the population of interest.
The probability of correctly rejecting the null hypothesis, i.e. rejecting the null hypothesis when it is false; defined as 1 minus the probability of a type II error (See type I and type II errors).
Power is 1-Beta and is defined as the probability of correctly finding statistical significance. A common value for power is .80
The probability of correctly detecting an actual toxic effect (EPA, 2000).
The probability of rejecting the null hypothesis when the alternate hypothesis is true, i.e. correctly identifying an effect when it is there.
the probability of detecting a treatment effect of a given magnitude when a treatment effect of at least that magnitude truly exists. For a true treatment effect of a given magnitude, power is the probability of avoiding Type II error, and is generally defined as (1 - b).
The chance (given by %) that a pre-determined effect in a population can be detected as statistically significant in a sample.
the probability of detecting an effect of a given size with a stated level of significance.
Statistical power is the probability of detecting a difference between two groups when one truly exists. Adequate power for a trial is widely accepted as 80%; that means there is a 20% chance of a type II error (not detecting a difference between the treatments when in fact a difference exists). Statistical power and the effect size of interest are taken into account when calculating sample size.
A study needs to have a specific level of 'power' in order to be able to reliably detect a difference that a treatment might cause. The study needs to have enough participants, who experience enough of the outcomes in question, to be able to come up with statistically significant results.