The Easiest Data Analysis Mistake to Make

This is a very simple and very pathological example that's easily ferreted out with a few more summary statistics (median, min, max) but it's a good illustration of the blind application of statistics. Short of visualization, non-parametric statistics really help with such things. Correlation is a fragile, linear measure, and things that are obviously correlated by inspection can easily appear mathematically uncorrelated -- points on a unit circle, for example. Likewise, the mean of any skewed distribution tells you very little, but that's the statistic that's always cited. Quantiles, medians, and non-parametric measures of correlation such as rank correlation are simple and often overlooked. They do a good job screening for pathological data sets like Anscombe's quartet and real world ones.

It's also worth mentioning "dumbbell" data sets. Two clusters of data, each of which have a independent, meaningful correlation in them, can easily leverage a linear regression into a meaningless line passing through the two clusters. That's a pretty common issue with high dimensional data (obviously you can see it in a 2D scatter plot), and it's not easily caught short of looking at regression diagnostic statistics.

I think, typically, if you've gone to the trouble of calculating variance and correlation, you would have also calculated the median and mode of these datasets. The differences would have been obvious with those basic analyses.

Statwing looks great!

Right now I do my data analysis in numpy, but this looks good for my Excel-based colleagues.

What library is doing the statistics?

So a concept you find in a beginner stats textbook is now 'news'? Definition of blogspam surely...

My favourite part of this (aside from the message) is that it links back to this exact discussion page.

An equally common mistake is to visualize without analyzing :-)

who doesn't look at the data range, min, max, mode???