Look at the histogram and think about what distribution one could reasonably impute from samples. And what you would set as bounds for "outliers", per your needs. While we're at it, let me also say that it might be useful to specify outlier bounds not just based on the spread in sample values, but the costs/payoffs they imply for your application.
If you are not doing something crazy, most reasonable people would agree with your judgement. Conversely, if you are making non-obvious inferences where reasonable people disagree, you are in murky water and no sophisticated statistical method will save you. Math is not magic; theorems merely recycle (launder) modeling assumptions into results.
>Look at the histogram and think about what distribution one could reasonably impute from samples. And what you would set as bounds for "outliers", per your needs. While we're at it, let me also say that it might be useful to specify outlier bounds not just based on the spread in sample values, but the costs/payoffs they imply for your application.
Only people with prior education/training in statistics are capable of doing this. The people who don't need a textbook.
Something like 60% of US adults read at or below the 6th grade level, and 25% of US adults struggle to comprehend graphs or charts entirely. Someone who has no idea what a standard deviation is can't intuit about distributions. I think you're dramatically overestimating the average person.
Yup. And thinking through the physical realities or whatever real world constraints exist.