"extreme observations" is a correct and usable phrase in written English.
It can be used to refer to observations that are considered to be unusually notable or noteworthy. For example, you could say, "I made some extreme observations in my field research that could have implications for future studies."
For describing distributions, the median was preferred over the mean, since the distributions are extremely skewed, and the mean can be severely affected by extreme observations, while the median is more robust.
We used the median contrasts method to address this problem because the median outcome represents an observed outcome that is easy to compute and is less vulnerable to extreme observations than other measures of central tendency.
As the resolution decreases (i.e., number of stations per grid box increases), extreme observations generally have less impact on the interpolated values.
Historically, people used to determine a species range by plotting the most extreme observations of that species on a map, drawing a polygon around them and calling it a day.
There is a general agreement amongst researchers that conventional higher order moments do indeed amplify the extreme observations and drastically affect scaling exponents.
The membership threshold (varTheta ) should operate on extreme observations.
This shows that our results are not driven by extreme observations for order imbalance.
These breaks are associated with extreme observations that were not identified as outliers neither corrected in Charles and Darné (2014).
Our decision to Winsorize the BMI distribution (as opposed to dropping the extreme observations) is driven by two factors.
This allows us to use the extreme observations without outliers having a big impact on the estimates.
This method is adept at exploiting all the available extreme observations, improving the efficiency of the estimation.
