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This is why one of my principles is to be skeptical of outliers. Often they are not real and therefore misrepresent the true data. It's one reason median is pr
by djoldman 1y ago
This is why one of my principles is to be skeptical of outliers. Often they are not real and therefore misrepresent the true data.
It's one reason median is preferred over mean, at the outset, as well as throwing out outliers just to see what things look like.
- yen223 1y agoThe lesson I took from this is that it is useful and important to dig into how any piece of data was sourced.
- dustincoates 1y agoSimilar to Twyman's Law: “Any figure that looks interesting or different is usually wrong.” https://en.m.wikipedia.org/wiki/Twyman%27s_law https://en.m.wikipedia.org/wiki/Twyman%27s_law
- pixl97 1y agoStill you should be aware of https://en.m.wikipedia.org/wiki/Black_swan_theory https://en.m.wikipedia.org/wiki/Black_swan_theory
- stdbrouw 1y agoThis advice is insane. Except in specific settings (where a sensor may be misbehaving, where a survey respondent clearly just picked random choices) outliers are really just outlying values and should be kept in the analysis, or at most clipped / winsorized. When submitting to a scientific journal, admitting that outliers were removed without first inspecting why they are there can be enough for an instant rejection, and rightly so.