A power analysis is a computation that determines the minimum sample size needed for an experiment given the necessary statistical power, effect size, and significance level. It aids in determining if an experiment or survey result is genuine and noteworthy or the product of chance. In this blog, the role of power analysis in the planning and design stage of research will be discussed briefly.
Defining power analysis
Tests of hypotheses are directly related to power analysis. Researchers are capable of making either a Type I error or a Type II error when testing their theories. Type II mistakes are generally the subject of statistical power. Let me give you an example to clarify this a bit.
Suppose you want to conduct primary data collection for a research topic. Now, the research respondents are 18 to 30 years old which you need. But you need people who not only have knowledge of the research topic but also have some experience in conducting research. So, eventually, there will be very few people left who are ideal for your research. That is what power analysis is, to identify the minimum sample size required for your research.
Normally, power analysis takes place before data gathering. Power analysis is primarily used to assist researchers in identifying the minimum sample size necessary to detect an effect of a particular test at the desired level of significance. Because bigger samples are frequently more expensive than smaller samples, the investigator prefers a smaller sample in theory, which is why power analysis is used. Significance testing is also best when using smaller samples which increases the chances of detecting true effects and reduces the risk of Type II errors.
Figure 1: Power analysis
Role of power analysis in the planning and design stage of research
The power analysis portrays an essential role not only in the planning and design stage of research but also in the successful conduct of research. The role is described as:
Statistical significance -
Power analysis provides the apparent advantage of verifying that the research or study's findings have statistical significance—that is, that the conclusion reached is accurate and cannot be explained by chance. This means that the study's findings can be put into practice knowing that the results will be advantageous to the company. It significantly increases the chance to detect true events and reduces the risk of Type II errors.
Less harm to the researchers -
Knowing that a study's findings are probably accurate reduces the potential of harm to users, especially when the findings are concerning actual people. A large amount of power ensures that no type I error will be returned from the study's findings.
Explains the causes of error in a sample population -
If a business intends to launch a new feature, it can do testing and reasonably be certain that the outcome is accurate. However, power analysis takes into account populations and subgroups within the larger groupings, giving researchers more control over the outcome. For instance, the researcher could make a costly error if a section of a community is not taken into account, the number of surveys, or the diversity of questions wasn't broad enough.
Though the roles are significant for the researchers, there are various challenges of power analysis that are also needed to be taken care of. The challenges are described below:
Historical Power Analysis -
A retrospective power analysis serves little to no purpose. It is comparable to performing a post-mortem. Retrospective power analysis only serves to pinpoint one of the most likely reasons why the project or research failed. The only way to rectify this is to put it down to experience and conduct an a priori power analysis the next time. At this point, there is no way to revive the research, it cannot be resolved, and it cannot be mended.
Larger Sample Sizes Can Also Find Small Effects -
The likelihood that a tiny effect will be discovered increases with the sample size. And it might not be worthwhile to identify that effect. A big sample size is not necessarily the solution and may just lead to the detection of a trivial minor effect.
Focus on Power Ignores Other Vital Results -
By concentrating just on one power, a study may overlook other important information and results. Confidence intervals and estimates are also crucial. A more significant range of discoveries can be shown by focusing more on the information's inherent value than on its increased power.
80 percent of assurances were false -
An 80 percent power claim doesn't actually tell us much about how valuable a given study is. The idea that a null hypothesis can be accepted or rejected just because the power is at or below 80% is a faulty one. For instance, it would not be considered supported if 40 pregnant women were evaluated and given vitamin C tablets, but the supplementation only resulted in the survival of one kid. But even just one life saved is priceless.
Figure 2: Role of the power analysis
Finally, we can conclude that power analysis can not only help us to detect true events but is also it is useful to reduce the risk of Type-II errors. We, at Regent Statistics, can help you to conduct power analysis not only in the planning and design stage of research but also in all other stages of research.