The Inefficacy of Chauvenet's Criterion for Elimination of Data Points

Abstract

Chauvenet's criterion is commonly used for rejection of outliers from sample datasets in engineering and physical science research. Measurement and uncertainty textbooks provide conflicting information on how the criterion should be applied and generally do not refer to the original work. This study was undertaken to evaluate the efficacy of Chauvenet's criterion for improving the estimate of the standard deviation of a sample, evaluate the various interpretations on how it is to be applied, and evaluate the impact of removing detected outliers. Monte Carlo simulations using normally distributed random numbers were performed with sample sizes of 5–100,000. The results show that discarding outliers based on Chauvenet's criterion is more likely to have a negative effect on estimates of mean and standard deviation than to have a positive effect. At best, the probability of improving the estimates is around 50%, which only occurs for large sample sizes.

Comparison and Analysis of Detection Methods for Typhoon-Storm Surges Based on Tide-Gauge Data—Taking Coasts of China as Examples

“…Chauvenet criterion (CC) [ 14 , 15 ] assumes that the samples obey the normal distribution, arguing that, out of N data points, the data points with a probability of occurrence of less than 1/2 N can be considered as outliers and determined as possible storm-surge events. Let be a value greater than 0; when , the following function relationship is satisfied: where is the residual-water-level data at a certain time, is the mean value of the residual water level within 1 year and N is the number of data points within 1 year.…”

“…Therefore, this paper attempts to calculate the threshold based on detecting the outlier. Four commonly used methods for detecting the outlier—the Pauta criterion (PC) [ 13 ], Chauvenet criterion (CC) [ 14 , 15 ] Pareto distribution (PD) [ 16 ] and kurtosis coefficient (KC) [ 17 ]—were used to calculate the annual threshold criteria for each tide-gauge station for outlier detection, after which the storm surge events at each station were detected. Li et al [ 18 ] used the Pauta criterion to detect outliers in groundwater time-series data and obtained ideal results by correcting outliers.…”

Correlation between objective and subjective tests for vehicle ride comfort evaluations

“…The first method is an outlier detection method. 18,19 In this method, the purpose is to discard values that are incompatible with the other values for the sample data set. These values are called outliers.…”

A home-lab to study uncertainties using smartphone sensors and determine the optimal number of measurements

“…These experiments could contribute to motivating students and showing them the necessity of considering uncertainty analysis. Several possible extensions related to non-normal statistics can be considered, such as Poisson distribution [4], distribution of maxima, Chauvenet criterion [17], or Benford's law [18].…”