Outlier Detection and Treatment in Textile Testing
topic
Statistical outlier identification in textile datasets uses Grubbs test for single outliers in normally distributed samples, Dixon Q-test for small samples, and Hampel identifier for contaminated distributions, distinguishing statistical outliers that may represent genuine extreme values from measurement errors, specimen defects, and data transcription errors requiring investigation and possible exclusion.
Role
Provides objective statistical criteria for identifying and handling extreme values in textile test datasets, preventing both arbitrary exclusion of valid extreme measurements that inflate apparent precision and inclusion of erroneous values from equipment malfunction or specimen defects that distort population parameter estimates.