Remove Outliers
The iceTEA tool uses a generalised extreme Studentized deviate (gESD) test to statistically identify whether there are any outliers within the exposure dataset, which is assumed to come from a single feature. Once the outliers have been identified and removed, the reduced dataset is plotted as kernel density estimates with the corresponding modal age, weighted mean and standard deviation, and reduced chi-squared statistic.
Details are described here: Jones et al., 2019, Quaternary Geochronology (preprint version here).
Upload exposure age data in the ‘standard’ input form (as .xlsx, .csv or .txt), including previously calculated exposure ages. Then choose the significant level for the outlier test.