- Last Updated: [[2020-12-28]] - One way to reduce the effect that an outlier has on typical summary statistics is to plot data on a logarithmic scale. - ""`But there is a problem with all these charts. The pattern of the points means all the attention is focused on the extremely high guesses, with the bulk of the numbers being squeezed into the left-hand end. Can we present the data in a more informative way? We could throw away the extremely high values as ridiculous (and when we originally analysed this data I rather arbitrarily excluded everything above 9,000). Alternatively we could transform the data in a way that reduces the impact of these extremes, say by plotting it on what is called a logarithmic scale, where the space between 100 and 1,000 is the same as the space between 1,000 and 10,000. `([Location 610](https://readwise.io/to_kindle?action=open&asin=B07N6D73FZ&location=610))"" - ""* `To get the logarithm of a number x, we find the power of 10 that gives x, so that, for example, the logarithm of 1,000 is 3, since 103 = 1,000. Logarithmic transformations are particularly appropriate when it is reasonable to assume people are making ‘relative’ rather than ‘absolute’ errors, for example because we would expect people to get the answer wrong by a relative factor, say 20% in either direction, rather than being, say, 200 beans off the true count regardless of whether they are guessing a low or high value. `([Location 4726](https://readwise.io/to_kindle?action=open&asin=B07N6D73FZ&location=4726))""