Datasets frequently include abnormal responses or data points known as outliers. These outliers can significantly impact the final results of primary tests, subsequently influencing the recommendations provided to key stakeholders based on these outcomes. As a result, many researchers adopt a cautious approach, sometimes opting to eliminate all data points identified as outliers.

Contrary to the instinctive caution exhibited by my peers when handling outliers, I find it intriguing that I am less conservative in this aspect. This deviation is noteworthy as I tend to be more conservative in most other methodological aspects of statistics compared to my contemporaries. In the context of outlier handling, I recognize that being an outlier does not inherently render the data flawed. While less egregious than disregarding outliers entirely, the removal of outliers can become excessive and counterproductive to achieving an accurate representation of a natural phenomenon.

To begin with, apart from the additional degree of freedom it introduces to the model, it is established that not every outlier influences the final result of a statistical model. Consequently, removing every outlier is often unnecessary. Moreover, the crucial consideration is whether an outlier, regardless of its influence, is valid—meaning it accurately reflects the value of the construct it seeks to measure. Removing valid outliers impedes the model's ability to accurately mirror reality. The exclusion of accurate outlier values diminishes the model's capacity to extrapolate from the measured regions of the constructs under study. Given that outliers often represent extreme values of the constructs of interest, their removal frequently hampers the accurate extrapolation (prediction) of both high and low scores.

So, what is the recommended course of action? My approach involves identifying outliers and other responses that can be confidently classified as careless responses (i.e., non-responses). Careless responses are those answered without consideration for the item's content, failing to reflect the actual level of the measured construct. Since careless responses often overlap with outliers, outlier detection analysis serves as a robust detection technique. When I encounter outliers, I diligently assess the plausibility of each response. If a response is deemed plausible but unique, I am inclined to retain it in the model. However, if the model's significance relies on outlier responses that I consider reasonable and accurate, I responsibly report both sets of results to stakeholders in a comprehensible manner.