What is a false discovery rate?
False discovery rate is the process involved in identifying type 1 errors in the null hypothesis, which must be adequately assessed to ensure that the collected data is viable (Chen et al., 2021).
Can a false discovery rate be avoided entirely? Explain.
False discovery rate can be adjusted but cannot be completely ruled out from a data set. Adjusting the p-value is one of the criteria used by the data experts to ensure that the errors are minimized in the data. The collected data goes through a series of steps before generating the expected insights used in making long-term and viable decisions. This infers that the key players must deploy the most appropriate techniques to identify the false positive the true positives to reach the expected precision level. This way, the organizations can be better positioned to take up other challenges that matter to them.
What was the outcome of the results of the use case?
The results showed false discovery rates and RFT were identical in the presented datasets. The dissimilarity level was higher than the expected similarity level, which infers that there was a lot that needed to be done to get the desired result. Samples also played an integral role in the desired results since the small samples affected the connection between RFT and FDR, which are commonly used by data experts to sown the significance level (Naouma & Pataky, 2019). The significance level is deemed vital in any experiment to rule out the errors that might affect the attainment of the desired goals in the investigation. Qualitative and quantitative measures are also equally important which for instance, in the sample size, the larger the sample, the more stable the results appeared upon simulation. Based on this case, the data upon simulation did not meet the expected data needs, which infers that the values brought about the errors which must be addressed using the proper techniques