speaking as a humble statistician, If x is your sample, given x = {8, 3, 7, 1, 3, 9} sample mean = (8+3+7+1+3+9)/6 = 5.166, doctors mean = (1+9)/2 = 5,

speaking as a humble statistician, If x is your sample, given x = {8, 3, 7, 1, 3, 9} sample mean = (8+3+7+1+3+9)/6 = 5.166, doctors mean = (1+9)/2 = 5, trimmed mean = (8+3+7+3)/4 = 5.25, median aka 50th percentile = (3+7)/2 = 5. ofcourse programmers will have none of this. They'll code up a fancy function called computeMean(x) in java or python or what have you, with proper types & unit tests & suchlike, and declare "the mean is 5.166", and call it a day. No amount of persuasion will convince them that the mean is unknown, because the population's distribution is unknown, so population mean can at-best be hypothesized to some constant c, and then you can run a hypothesis test to see if you can disprove said hypothesis at some alpha level. ofcourse with a sample size of n=6 your power will take a beating and type-2 errors will spike, but who cares about these esoterica when our famed intern has coded up the mean function itself in one afternoon! Snark aside, every summary statistic has widely varying properties. Sample means will asymptotically converge to the population ("true") mean as sample size grows. Sample means are an unbiased estimator of the population mean, so they have the mvue i.e. minimum variance, the variance decreases linearly with sample size. But that doesn't mean one should ditch biased estimators. Choosing a clearly biased estimator such as a trimmed mean instead of the sample mean, which is what they've done here, can definitely result in a huge breakthrough (at the expense of higher variance but that's ok).