What is a power calculation good for?
Power calculation helps a researcher determine the required sample size for a study. It is done at the planning stage of a trial.
Saves you from going down an impossible research path due to limited resources for patient recruitment.
Eg. The effect (correlation, standardized difference in means, etc.) is too small to be detected without a huge sample, making the research project unfeasible from the get-go.
Provides an amazing opportunity to precisely define the statistical analysis plan beforehand, helping you frame the research question in a statistical language.
To compute the required sample size, the study design and statistical analysis plan must be clearly defined.
Saves you the stress of having data that do not show statistically significant effects. This in turn avoids the pressure of “massaging” the data, looking for something to publish!
This goes back to the first point – if you don’t have enough data to have a good chance of detecting the effect of interest, your p-values will not cross the 0.05 threshold and you will be stuck trying to p-hack your way to a significant effect.
Prevents you from publishing rubbish, which can hurt your credibility.
A well-designed study has a lot more of chance of being valuable to your field.
Gives more credibility to your grant proposals, since statisticians on the evaluation committee will immediately see that your statistical methodology was done by an expert.
Statisticians can clearly see when a methodology plan is written by a fellow statistician as opposed to someone who struggled their way through computations using G-power or other software. This hurts your chances of getting funding.
Removes the unnecessary stress of wondering if your statistical analysis even makes sense.
By doing this work upfront, you avoid pushing back the inevitable time when you have to decide which sort of statistical approach you will bring to the project.
Since you now have a clear plan for the statistical analysis, the data collection part will be more guided, preventing you from wasting resources collecting useless or wrong data.
Data collection can be extremely expensive, especially when working with human beings. A well thought-out statistical analysis plan can avoid collecting too much data, especially when every data point counts.
Statisticians are friendly folks : involving a professional statistician allows you to work with amazing people!
At least, I like to think people enjoy working with me. Reach out if you need help with statistics 🙂
