Power Analysis

What is Power Analysis?

Power analysis is a technique used in experiment design to determine the sample size necessary to detect an effect of a given size with a specified degree of confidence.

It is usually done before the data collection, to obtain the smallest sample that can produce the desired experiment’s results. Therefore, obtaining the correct sample size is an aspect of any research that has important practical and financial implications.

Sample size is directly related to hypotheses testing and its two types of errors. Type I error represents the rejection of a true null hypothesis, while type II error is the non-rejection of a false null hypothesis. Generally speaking, they represent the possibility of reaching a conclusion, when in fact there is no support for it by the research done.

Why is Power Analysis Important?

Power analysis is a very important research tool, as it allows researchers to specify the minimum size of a sample that can be used in an experiment to reach conclusions within a given margin of error. Thus, it helps ensure that the results obtained from the sample can be extrapolated to the entire population.

Moreover, obtaining the minimal sample size can have significant consequences, as doing an experiment with very large samples may be difficult and costly. It can also determine whether the experiment can be conducted or not, or that a specific error level may be difficult to achieve in practice.

Power Analysis + LogicPlum

Determining the right sample size may require long calculations and statistical knowledge. LogicPlum’s platform solves this difficulty for its users by giving them the capability to automatically create machine learning based models.

This tool works by trying and testing hundreds of different algorithms and selecting the best solution based on a performance metric. All computations, including those related to power analysis, are done by the platform.

This ensures that users have access to the latest technologies and that they are applied correctly. As a result, the obtained models can be used with the confidence that they provide statistically significant results.

Guide to Further Reading

For those interested in applying Python:

McCullum, N. (2020). Statistical Power Analysis in Python. Available at https://nickmccullum.com/power-analysis-in-python/