when assessing the winsorized level understanding the nature of outliers and their reasons can help determine the appropriate level outliers can influence the statistical analysis so a higher winsorization level may be beneficial if they unduly influence results however a lower level may be more appropriate if the goal is to preserve some of the data s original characteristics when picking the level gauge your interest in how important keeping the original data s composition is 
in many cases data domain knowledge is essential in setting the winsorization level consider any data set and what the typical range of values would be without historical implicit knowledge of the industry it would be much more challenging to identify bad data in some cases experimentation is crucial in observing how the winsorized mean changes with varying levels winsorized mean and real world situationsmore specifically there s a handful of situations or industries where winsorized mean makes more sense than other forms of measurement these real world situations may include but aren t limited to the categories below market volatility can have a material impact on financial data stock prices asset returns and other financial indicators may display extreme levels in the world of finance and investing the impact of severe price volatility and outliers can be lessened when financial data estimates are computed using the winsorized mean distributions of salaries or payroll within businesses can occasionally be very skewed this is especially true in sectors where there is a significant income gap or sectors that materially reward those who have been in the industry long or penalize those who are just starting their careers by minimizing the impact of abnormally high or low incomes the winsorized mean can assist in providing a more accurate measurement of the typical pay range because of uncommon medical illnesses or extreme measures medical data may contain outliers health related indicators like blood pressure cholesterol levels or patient recovery durations can be better understood should extremes be removed for example information may be more helpful to know regarding a collective average of patients should that data set not be skewed by any abnormally high or abnormally low medical readings due to a variety of variables some children may have unusually high or low test results it may not be as useful to incorporate these abnormal test scores when evaluating a specific cohort s performance therefore an assessment s average score can be calculated using the winsorized mean to remove any negative or positive implications that student may have when evaluating how a specific teacher or course was perceived on a very similar note when assessing customer satisfaction ratings outliers may appear because a tiny percentage of consumers provided extremely good or negative comments in the example above regarding a class perhaps a single disgruntled student brings down the course evaluation score winsorized mean can help reduce the influence of these extreme scores and produce a more realistic picture of overall happiness with seemingly more uncommon occurrences or extreme weather conditions occurring there may be situations where environmental data without these extremes are useful for example consider a measure of average air quality or the amount of water contamination abnormally high or low levels of contamination in either context may mislead decision makers in understanding what the average daily situation may be for example environmental economic resources may be misallocated example of how to use winsorized meanlet s calculate the winsorized mean for the following data set 1 5 7 8 9 10 34 in this example we assume the winsorized mean is in the first order in which we replace the smallest and largest values with their nearest observations the data set now appears as follows 5 5 7 8 9 10 10 taking an arithmetic average of the new set produces a winsorized mean of 7 7 or 5 5 7 8 9 10 10 divided by 7 note that the arithmetic mean would have been higher 10 6 the winsorized mean effectively reduces the influence of the 34 value as an outlier or consider a 20 winsorized mean that takes the top 10 and bottom 10 and replaces them with their next closest value we will winsorize the following data set 2 4 7 8 11 14 18 23 23 27 35 40 49 50 55 60 61 61 62 75 the two smallest and two largest data points 20 of the 20 data points will be replaced with their next closest value thus the new data set is as follows 7 7 7 8 11 14 18 23 23 27 35 40 49 50 55 60 61 61 61 61 the winsorized mean is 33 9 or the total of the data 678 divided by the total number of data points 20 winsorized mean vs other measurementsthere are several other common forms of mean each of which slightly vary from winsorized mean also there are other measurement such as median that give comparable but different information in general the winsorized mean is designed to be more resistant to outliers compared to other types of means those other types of measurement may include can winsorized mean handle multiple outliers yes winsorized mean can handle multiple outliers within the dataset it effectively replaces the specified percentage of extreme values regardless of the number of outliers present by replacing multiple outliers with less extreme values the winsorized mean remains less sensitive to the influence of these outliers can winsorized mean be used with non numeric data not really winsorized mean is primarily designed for numeric data as it involves replacing numerical outliers with other numerical values for non numeric data such as categorical variables or text data other robust statistical measures or preprocessing techniques may be more appropriate 