Central limit theorem is one of the most fundamental theorems in probability and statistics. The theorem states that sampling distribution of the mean of any independent random variables approaches normal as the sample size increases under certain conditions. Below I created a Shiny application to visualize central limit theorem in effect. Random samples are generated from a selected population distribution to visually assess the distribution of their means against the theoretical asymptotic normal distribution.
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One of the benefits I personally enjoy as a new TA is getting a refresher on the materials I first learned a few years back. Especially when I see students with difficulties/confusions over materials that I have been taking for granted, it gives me a chance to have a second look over the materials with a hope of a giving a better explaination. One such concept was the difference between categorical and quantitative variables during Statistics I tutorial sessions.
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