It is normally the case that power analysis is conducted before the collection of data. The purpose of the power analysis underlying it is to assist researchers in determining the smallest sample size suitable for detecting the effect of a given test at the desired significance level. To optimise significance testing, investigators should employ power analysis because it is ideally desired that they use a smaller sample because larger samples are often more expensive than smaller samples. Smaller samples also allow for greater precision.

A great deal of the power in analysis depends on the desired power level. It is usually assumed that the target power level will be 0.80. However, the researcher performing the power analysis can specify a higher power level, like 0.90, which means there is a 90% chance that they will not commit a type II error if they do. The level of significance that a researcher chooses to use in power analysis is one of the most stringent factors. For example, suppose the researcher sets a significance level of 0.001 in power analysis.

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Only in these cases can the alpha level of 0.001 be used if the researcher is primarily concerned with avoiding a type I error. Therefore, an alpha level of 0.001 is only appropriate when the researcher is primarily concerned with preventing this error. An analysis’s power is also influenced by the association between the variables and the relationship’s strength. The stronger the association between the two variables is, the stronger the power analysis’s power.

Consequently, a strong association leads to a greater value of power when it comes to power analysis. Sensitivity is one factor that affects the power of power analysis. In power analysis, sensitivity refers to the number of true positives compared to the total amount of false negatives and true positives. In other words, this effect of power analysis is to identify the data that has been accurately corrected.

It should be noted, however, that highly sensitive data will provide the researcher with data that will yield a higher value of power in power analysis, which will result in a lower likelihood of the researcher committing a Type II error as a result of this data. As a result of the variation of the dependent variable, the researcher is also more likely to commit Type II errors. Therefore, the power value will be lower in power analysis when the dependent variable is more variable.