Why is removing a data point identified as a 'true outlier' potentially harmful to analysis?
Answer
Removing it biases the model toward the average case, masking important real-world variation.
True outliers represent rare but valid extremes in the population being studied; discarding them results in a model that underestimates the true variability present in reality.
Related Questions
What effect does a single, extremely high outlier have on the sample mean and standard deviation?What is often cited as the most common and simplest cause of an outlier?What specific type of data entry error involves accidentally swapping adjacent digits during transcription?In survey data, what term describes a respondent providing a plausible but untrue answer because they rushed through the questionnaire?What characteristic typically distinguishes Process Failures from isolated Manual Mistakes when examining the data?What scenario exemplifies a Process Failure due to Instrument Malfunction creating a systematically biased group of data points?Why is removing a data point identified as a 'true outlier' potentially harmful to analysis?What critical element is required for an analyst to differentiate reliably between an equipment error and a genuine, rare natural event?If a measurement of 8 feet is recorded when measuring the height of adult men, what does this suggest about the value?What is the prudent final step in the investigative checklist when an outlier surfaces?What is the consequence if a systematic measurement error, such as improper calibration, is mistakenly treated as a natural extreme?