# Handling Skewed Data: A Comparison of Two Popular Methods
## 4 Discussion
First, in most cases, rates were not missing, but the counts for accepted simulated samples were less than 10,000. That caused the setting to be rejected. Particularly in the case of calculating power rates, the two groups are from the same distribution with different parameters, which made it harder to apply the two filters (both groups should not be normally distributed before the use of log transformation and normally distributed after the application of log transformation). Although being less than 10,000 caused the estimates to vary as a response to changing the random seed, it gave the same conclusion. For example, if the GLM had a higher power rate in one sitting of parameters, it kept having a higher power rate even if we changed the seed. Yet, we preferred not to include these settings because we needed the estimates to be reproducible. Second, in rare cases, none of the samples' normality issues were resolved with log transformation, so we had zero accepted simulated samples. As a result, our comparison does not apply to that parameter combination. In conclusion, we did not consider missing rates as an issue since GLM had a higher power rate, as well as the \(t\)-test had closer mean difference estimates in all other accepted settings. Our results were consistent across parameter settings and sample sizes (\(N=25,\ 50,\ 100\)), so we expect that the difference in sample sizes will not affect the method choice no matter what the sample size effect is over the method performance itself. After analyzing the data from our real-life examples, we recommend reporting the results from the \(t\)-test with log-transformed data for the lipid data example and catheters data example, since the best fit for both examples was a gamma distribution. Because the catheters data example had larger sample size (\(n=592\)) than used in our simulations, we conducted additional simulations with sample size of 296 per group. Though not reported, these results concurred with our reported findings at lower sample sizes. We followed the same steps for the third example (the pharmaceutical and computer data example), which had the exponential fit as the best fit. We conducted additional (though not reported) simulations with 16 subjects per group, and again observed results that agreed with our reported findings. Thus, our recommendation is to report the GLM method results. Therefore, for any Bio-application research, studying the appropriate statistical distribution that fits the dependent variable can help us to determine if a parametric model can reasonably test the data after log transformation is used. Alternatively, it would probably be better to abandon the classic approach and switch to the GLM method. **Author Contributions:** Conceptualization, H.M.H. ; Investigation, H.M.H., R.T.S., R.A. and K.A.K. ; Methodology, H.M.H. ; Software, H.M.H. and R.A.; Writing--original draft, H.M.H., R.T.S., R.A. and K.A.K. ; Writing--review and editing, H.M.H., R.T.S., R.A. and K.A.K. All authors have read and agreed to the published version of the manuscript. **Funding:** This research received no external funding. **Conflicts of Interest:** The authors declare no conflict of interest.