Factors That Affect Power in Machine Learning?

Data Science, AI and ML
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There are certain factors that affect power in power analysis:

The desired power level affects the power in analysis to a great extent. The desired power level is typically 0.80, but the researcher performing power analysis can specify the higher level, such as 0.90, which means that there is a 90% probability the researcher will not commit a type II error.

One of the stringent factors in power analysis is the desired [level of significance Suppose the researcher specifies 0.001 as the [level of significance. In this case, the power in power analysis will be decreased. Thus, an alpha level of 0.001 is applicable only in those situations in which the researcher is mainly interested in avoiding a Type I error.

Another factor affecting the power of an analysis is the strength of association or the strength of relationship between the two variables. The greater this strength of association is, the more the power in the power analysis. This means that a greater strength of association leads to a greater value of power in power analysis.

A factor called sensitivity affects the power in power analysis. The term sensitivity refers to the number of true positives out of the total of true positives and false negatives. In other words, this effect of power analysis recognizes the truly corrected data. This means that highly sensitive data will yield data with higher value of power in power analysis, which means that the researcher will be less likely to commit Type II error from this data.

The variation of the dependent variable also affects the power. The larger the variation in the dependent variable is, the greater the likelihood of committing Type II errors by the researcher. This means that the value of the power will be lower in power analysis.