Statistical Experiments With Resampling

Most people working with data make observations and then wonder whether these observations are statistically significant. And unless one has some formal training on statistical inference and past experience in running significance tests, the first thought that comes to mind is to find a statistician who can provide advice on how to conduct the test, or at least confirm that the test has been executed correctly and that the results are valid. There are many reasons for this. For a start, it is often not immediately obvious which test is needed, which formulas underpin the test principles, how to use the formulas, and whether the test can be used in the first place, e.g. because the data do not fulfil necessary conditions such as normality. There are comprehensive R and Python packages for the estimation of a wealth of statistical models and for conducting statistical tests, such as <a href="https://www.statsmodels.org/stable/index.html" rel="noopener ugc nofollow" target="_blank">statsmodels</a>. Still, without full appreciation of the statistical theory, using a package by replicating an example from the user guide often leaves a lingering sense of insecurity, in anticipation of severe criticism once the approach is scrutinised by a seasoned statistician. Personally, I am an engineer that turned into a data analyst over time. I had statistics courses during my undergraduate and postgraduate studies, but I did not use statistics extensively because this is not typically what an engineer does for a living. I believe the same applies to many other data analysts and data scientists, particularly if their formal training is for example in engineering, computer science or chemistry. <a href="https://towardsdatascience.com/statistical-experiments-with-resampling-cb9ad2a5e66b">Read More</a>