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There are some long term diseases where the severity of your symptoms tend towards a 'normal'. So imagine plotting out the severity of the symptoms say, every day or every week, then drawing a line of best fit through them.

So over the long term, severity may change slowly. However in the short term, statistically speaking, it'll tend to be that if symptoms have been super bad for a short while, it'll tend to get better. And if symptoms have been very good for a short while, it'll tend to get worse.

There's a name for this trend. I'm not sure if it's actually a cognitive bias, it could be more of a statistical fallacy type thing. I can't remember the name... and I've searched for it in every way I can think of in google but no luck. I originally read about it in the context of pseudoscience treatments. So someone has a long term condition, they have a very 'bad year', they go see say... a homeopath, and then they get a bit better and they ascribe their improvement to the homeopathy where as in fact, statistically speaking they were likely to have gotten a bit better on their own.

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The statistical phenomenon you are referring to is called regression to the mean. It describes the fact that extreme measurements which are affected by random fluctuation will tend to be closer to the average in subsequent measurements. This phenomenon is not limited to clinical symptoms but occurs for any measurement that is selected for its extremity.

As an example, you let students take two tests that are designed to assess the same underlying aptitude. Then you look at the 5% best and the 5% worst performers in the first test. In the second test, the score of the high performing students will likely drop to some extent and the score of the low performing students will likely rise to some extent, even though the aptitude has not changed. This is because the test results are affected by random variation (they are not perfectly correlated) that is independent from the aptitude.

The failure to take statistical regression into account gives rise to the so-called regression fallacy. The regression fallacy refers to the tendency to attribute the change of extreme scores to spurious causal reasons. As a frequent example, if a football team loses some games and fires the coach, subsequent increases in performance may be explained with this fact even though it was just due to chance.

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