Analysis of Statistical Outliers with Application to Whole Effluent Toxicity Testing

Abstract

In this analysis, low-value outliers were detected in five data sets obtained from laboratory records. The effect of removing the outliers by three methods of data rejection (asymmetrical and symmetrical trimming and Winsorization) revealed that all three methods slightly increased the mean and reduced the variance of the data sets. These opposing effects on the results of a hypothesis test of means were examined in the context of passing or failing a regulatory requirement of no significant difference between an effluent concentration and a control. Analysis by statistical resampling of one data set showed that while all data rejection methods reduced the level of Type II error in a hypothesis test of no difference in reproduction between a test concentration and a regulatory limit, asymmetrical trimming was the best in this regard. Water Environ. Res., 73, 575 (2001).

Assessment of Patient Factors, Surgeons, and Surgeon Teams in Immediate Implant-Based Breast Reconstruction Outcomes

“…Trend lines were calculated with teams that had a skin necrosis or infection rate within the fifth to 95th percentiles. [21][22][23] …”

Vaccinia virus–specific molecular signature in atopic dermatitis skin

“…Resulting changes in expression were correlated with eczema area and severity index (EASI) score of the AD patient population. In order to reduced the level of Type II error,18 due to the small sample size, the symmetrical trimming of the highest and the lowest EASI scores was conducted. The R (correlation coefficient) and P (two-tailed probability) identified using llinregress() function of the Python Statistical Module 10.…”

Kualitas Laporan Keuangan: Latar Belakang Pendidikan Dan Pengalaman Internasional Cfo

“…Hasil screening data menunjukkan bahwa terdapat outliers univariate dan tidak terjadi outliers multivariate (Lampiran 2). Penelitian ini menggunakan teknik winsorizing untuk mengatasi outliers univariate dengan mengubah data agar lebih dekat dengan data lain yang bukan merupakan outliers (Buckley & Georgianna, 2001). (Hair et al, 2019).…”