![]() ![]() For example, if you have a regression model that can be conceptually described as: BMI = Impatience + Race + Gender + Socioeconomic Status + IQ This works if you have a very small number of variables you want to control for, but as you've rightly discovered, this rapidly falls apart as you split your data into smaller and smaller chunks.Ī more common approach is to include the variables you want to control for in a regression model. The easiest, and one you came up with, is to stratify your data so you have sub-groups with similar characteristics - there are then methods to pool those results together to get a single "answer". How distant are the outliers from other observations? Some observations considered as outliers (according to the techniques presented below) are actually not really extreme compared to all other observations, while other potential outliers may be really distant from the rest of the observations.There are many ways to control for variables.For instance, the slope of a simple linear regression may significantly varies with just one outlier, whereas non-parametric tests such as the Wilcoxon test are usually robust to outliers. Whether the tests you are going to apply are robust to the presence of outliers or not.If results change drastically due to some influential values, this should caution the researcher to make overambitious claims. It also happens that analyses are performed twice, once with and once without outliers to evaluate their impact on the conclusions. In other fields, outliers are kept because they contain valuable information. In some domains, it is common to remove outliers as they often occur due to a malfunctioning process. The domain/context of your analyses and the research question.Removing or keeping outliers mostly depend on three factors: After their verification, it is then your choice to exclude or include them for your analyses (and this usually requires a thoughtful reflection on the researcher’s side). This article will not tell you whether you should remove outliers or not (nor if you should impute them with the median, mean, mode or any other value), but it will help you to detect them in order to, as a first step, verify them. Some statistical tests require the absence of outliers in order to draw sound conclusions, but removing outliers is not recommended in all cases and must be done with caution. In this article, I present several approaches to detect outliers in R, from simple techniques such as descriptive statistics (including minimum, maximum, histogram, boxplot and percentiles) to more formal techniques such as the Hampel filter, the Grubbs, the Dixon and the Rosner tests for outliers.Īlthough there is no strict or unique rule whether outliers should be removed or not from the dataset before doing statistical analyses, it is quite common to, at least, remove or impute outliers that are due to an experimental or measurement error (like the weight of 786 kg (1733 pounds) for a human). Extreme values are statistically and philosophically more interesting, because they are possible but unlikely responses. ![]() Her or his weight is most probably 78.6 kg (173 pounds) or 7.86 kg (17 pounds) depending on whether weights of adults or babies have been measured.įor this reason, it sometimes makes sense to formally distinguish two classes of outliers: (i) extreme values and (ii) mistakes. For instance, a human weighting 786 kg (1733 pounds) is clearly an error when encoding the weight of the subject. Outliers can also arise due to an experimental, measurement or encoding error. For example, it is often the case that there are outliers when collecting data on salaries, as some people make much more money than the rest. Indeed, someone who is 200 cm tall (6’7” in US) will most likely be considered as an outlier compared to the general population, but that same person may not be considered as an outlier if we measured the height of basketball players.Īn outlier may be due to the variability inherent in the observed phenomenon. Enderlein ( 1987) goes even further as the author considers outliers as values that deviate so much from other observations one might suppose a different underlying sampling mechanism.Īn observation must always be compared to other observations made on the same phenomenon before actually calling it an outlier. An outlier is a value or an observation that is distant from other observations, that is to say, a data point that differs significantly from other data points. ![]()
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