Thread by @SanjeevSanskrit

After huge demand, giving you distribution plots of interview (personality) & written marks in UPSC. First image is for interview marks in 2018. As clearly seen, marks of M (blue) are clearly shifted by around 10 marks higher from non-M (orange) candidates. Isn't it interesting?+

The written marks of 2018 have more interesting pattern. M marks (blue) show perfect bell curve (actually too perfect for small sample). But non-M marks (orange) seem as if half of candidates' have marks pulled towards lower to balance for higher marks of top half of candidates.+

In 2017, interview marks show same trend but margin of difference between M and non-M is lesser. 2016 has a bit uneven marks pattern for non-M but a smooth curve for M. Interesting!+

If we compare written marks for 2017 and 2016, they are close. I won't infer much given the sample size difference in M and non-M candidates. So instead of being too finicky, I would say they are broadly same.+

Note that this is what data shows. Why this is happening is not something data can suggest with this info. However I did plot category wise interview marks to see if story is different. But what I found was that for each of OBC and General, M still outperformed non-M every year+

I would also provide charts for written performance over years. I leave it to people to bring out inferences. What is important though is that on average OBC non-M seem to be having biggest shortfall compared to OBC M in interviews. It was 10.07 in 2018 and 9 in 2016.

Here is the thread of my earlier tweets on this issue. Some data has been revised as news agencies had published wrong list of selected Muslims earlier.

Since some are confused about what these graphs mean, here it is. These are distribution plots. X axis represents marks. Higher marks are on right side as in most graphs. Y axis is proportion of candidates who got particular marks. Put simply, these are histograms+

When you reduce the interval of each bar in histogram and represent proportion of candidates in y axis it becomes this distribution plot. You can interpret it just like any line graph for most purposes.