I'm very conservative about removing outliers, but the times I've done it, it's been either: * A suspicious measurement that I didn't think was real data. You should be worried about outliers because (a) extreme values of observed variables can distort estimates of regression coefficients, (b) they may reflect coding errors in the data, e.g. Really, though, there are lots of ways to deal with outliers … Grubbs’ outlier test produced a p-value of 0.000. The issue of removing outliers is that some may feel it is just a way for the researcher to manipulate the results to make sure the data suggests what their hypothesis stated. I have tried this: Outlier <- as.numeric(names (cooksdistance)[(cooksdistance > 4 / sample_size))) Where Cook's distance is the calculated Cook's distance for the model. If you use Grubbs’ test and find an outlier, don’t remove that outlier and perform the analysis again. o Since both criteria are not met, we say that the last data point is not an outlier , and we cannot justify removing it. the decimal point is misplaced; or you have failed to declare some values $\begingroup$ Despite the focus on R, I think there is a meaningful statistical question here, since various criteria have been proposed to identify "influential" observations using Cook's distance--and some of them differ greatly from each other. We are required to remove outliers/influential points from the data set in a model. Data outliers can spoil and mislead the training process resulting in longer training times, less accurate models and ultimately poorer results. Dataset is a likert 5 scale data with around 30 features and 800 samples and I am trying to cluster the data in groups. Along this article, we are going to talk about 3 different methods of dealing with outliers: Because it is less than our significance level, we can conclude that our dataset contains an outlier. If I calculate Z score then around 30 rows come out having outliers whereas 60 outlier rows with IQR. The output indicates it is the high value we found before. If new outliers emerge, and you want to reduce the influence of the outliers, you choose one the four options again. Sometimes new outliers emerge because they were masked by the old outliers and/or the data is now different after removing the old outlier so existing extreme data points may now qualify as outliers. For example, a value of "99" for the age of a high school student. Outliers, Page 5 o The second criterion is a bit subjective, but the last data point is consistent with its neighbors (the data are smooth and follow a recognizable pattern). 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