Data acquired and accumulated through research and observations can be inferred and interpreted with the help of statistics. Statistical analysis is the most reliable and dependable method of procuring the best and most accurate results on any given topic. This is where statistical power enters the arena.
Statistical power has established itself as a crucial element in the present day. To eliminate and deal with Type II errors that may prove to be menacing and potentially dangerous, (especially in pharmaceutical research) statistical power is crucial.
There are two types of errors that exist in statistical research, and they are type I and type II errors. Type I errors are those errors when a researcher rejects a true hypothesis as true, and type II errors are the exact opposite. To control the occurrence of type II errors, statistical power has been created. Statistical power was specifically designed to prevent null hypotheses from getting accepted as true. Since the offset of statistical power against type II errors, such errors have been controlled and prevented. Statistical power has been a very useful tool in researches and experiments.
Given the growing need for evidence-based practices in the world today, statistical power has done much in the world. Instead of accepting as true what is actually a null hypothesis, statistical power helps the researcher to identify the difference. Considering the dangers of taking a null hypothesis as true, statistical power acts as the probability (1-β) of rejecting null hypothesis when it is false. Statistical power ensures that the null hypothesis is rejected so it allows the researcher to avoid type II errors. Statistical power must be kept correspondingly high. The more the statistical power, the less the chance of having type II errors.
The analysis on Statistical Power is called Power Analysis. To analyze statistical power through power analysis, an analysis can be done both on data collected prior and post. Statistical power usually depends upon the desired power level and the desired level of significance in the test. Here, statistical power particularly identifies the level or possibility of preventing a type II error. On most occasions, the researcher takes the power level at 0.80, or 80% chance of not making the error. The level of significance signifies that a sample is probably about to get linked with the population. For instance, if the level of significance is 5%, then the sample drawn should have at least 5% characteristics of the population from where it has been drawn in statistical power. Statistical power is also decided by the strength of association or the effect size in the population. In statistical power, the effect size or the strength of association generally refers to the strength of association between the two variables. Hence, the greater the effect size, the more the statistical power. A greater effect size accentuates a greater Statistical power. The sensitivity of the data and the size of the sample also determine statistical power. In statistical power, sensitivity refers to the number of true positives out of the total of true positives and false negatives. In layman terminology, sensitivity relates only to data which is totally correct. This in turn implies that high sensitivity will give way to good data and finally a high statistical power. With high statistical power, there is access to data which has fewer type II errors.
The determination of the sample size of past data is very important in statistical power. This sample size keeps the significance of statistical power high, thereby denoting a larger sample size. With greater statistical power, errors (like type II errors) can be slowly prevented and controlled.
