The quickest and safest approach to discovering therapies that benefit people is through carefully executed clinical studies. A clinical trial is a study to provide specific answers about novel treatments, vaccines, diagnostic techniques, or novel applications of existing medications. Clinical trials evaluate new medications, tests, and treatments.

There are various phases of clinical studies. During a study, the expertise of a Chief Medical Officer Consultant contributes to the accumulation of additional knowledge regarding the potential therapy, its associated risks, its potential efficacy, and its impact on quality of life. The exploratory novel treatments or procedures that show the most promise are then put through human clinical trials after being tested in the lab and on animals.

What is Statistical Power?

A significance test’s statistical power, also known as its sensitivity, refers to the probability that it will identify an effect when such an impact occurs.

A real effect exists between two variables in a population and has a value greater than zero. In most cases, an effect can be deduced from a discernible gap between two or more groups or a connection between two or more variables.

High power in a study denotes the possibility that the test will identify the actual effect. Your experiment has low power, meaning there is only a limited possibility of discovering a true effect or the data will likely be skewed by random and systematic error. Both of these issues are problematic.

The size of the sample, the magnitude of the effect, and the level of statistical significance are the primary factors determining power. A power analysis may be performed to ascertain the required number of participants for a study.

Why Does Power Matter In Statistics?

When making inferences about a population based on information obtained from a sample, it is essential to have sufficient statistical power.

The testing of hypotheses begins with the formulation of null and alternative hypotheses: a null hypothesis positing that there will be no effect, and an alternative hypothesis predicting that there will be an effect (your actual research prediction).  

This exercise aims to amass enough information from a representative sample to conduct a statistical analysis to determine the feasibility of rejecting the null hypothesis in favor of the alternative hypothesis.

If you don’t make sure that your study has enough power, it may not be able to identify a real effect and result in a waste of resources. It may even be immoral to gather data from participants due to potential bias (especially in clinical trials).  

Power increases with:

  • Sample size (n): the reliability of a sample is directly proportional to the size of the sample (the smaller the sample, the larger the error). Therefore, it should be no surprise that increasing the sample size would increase the statistical power.
  • The extent to which a particular alternative hypothesis departs from the null hypothesis is referred to as the effect size.
  • The criteria for determining whether the test results are statistically significant (p-value) 

Take Away

A sample size calculation can be done for almost all quantitative studies. On the other hand, they could not be of much use in the preliminary stages of exploratory research, when there are just a few data points available on which to base the computations (though this may be addressed by performing a pilot study first) using the data from that).  

Calculating the appropriate size of a clinical trial sample is an essential step, given that most of these studies focus on determining the degree to which different treatments differ. An evaluation of the sample size is essential to the conduct of any clinical trials.