Tl;Dr; I'd like to implement histogram_trimmed_mean

"A truncated mean or trimmed mean is a statistical measure of central 
tendency, much like the mean and median. It involves the calculation of the 
mean after discarding given parts of a probability distribution or sample 
at the high and low end, and typically discarding an equal amount of both. 
This number of points to be discarded is usually given as a percentage of 
the total number of points, but may also be given as a fixed number of 
points." https://en.wikipedia.org/wiki/Truncated_mean 

Cloudwatch added support for trimmed 
mean. 
https://aws.amazon.com/about-aws/whats-new/2021/07/amazon-cloudwatch-supports-trimmed-mean-statistics/
 
There is also a few mention of why trimmed mean can be useful for teams 
here https://www.youtube.com/watch?v=_uaaCiyJCFA (skip the first 8 minutes)

I will use cloudwatch as an example. If you have a metric, you can 
calculate pXX.XX to get percentiles. p50, p99, or p99.9. This is similar to 
histogram_quantile(0.5, a_metric), histogram_quantile(0.99, a_metric) and 
histogram_quantile(0.999, a_metric). For trimmed mean, it looks very 
similar. tmXX.XX[:XX.XX]
tm99 is the with the last 1% removed and tm1:99 is the mean with the first 
1% and the last 1% removed.

I envision histogram_trimmed_mean(lower, up, metric) working in a similar 
way.

tm99 -> histogram_trimmed_mean(0, 0.99, metric)
tm1:99 -> histogram_trimmed_mean(0.01, 0.99, metric)

Similar to how histogram_quantile works, histogram_trimmed_mean would use 
the values gathered in the different buckets and extrapolate the trimmed 
mean.

Another alternative would be if it was possible to do something similar to 
avg_over_time(histogram_quantilte(0.01, metric) < metric < 
histogram_quantilte(0.99, metric))

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