# how to justify removing outliers

Because it is less than our significance level, we can conclude that our dataset contains an outlier. 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. Dataset is a likert 5 scale data with around 30 features and 800 samples and I am trying to cluster the data in groups. Another way, perhaps better in the long run, is to export your post-test data and visualize it by various means. If I calculate Z score then around 30 rows come out having outliers whereas 60 outlier rows with IQR. 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. 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. The second criterion is not met for this case. Along this article, we are going to talk about 3 different methods of dealing with outliers: 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. \$\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. Determine the effect of outliers on a case-by-case basis. 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). Data outliers can spoil and mislead the training process resulting in longer training times, less accurate models and ultimately poorer results. the decimal point is misplaced; or you have failed to declare some values If new outliers emerge, and you want to reduce the influence of the outliers, you choose one the four options again. Really, though, there are lots of ways to deal with outliers … If you use Grubbs’ test and find an outlier, don’t remove that outlier and perform the analysis again. outliers. The output indicates it is the high value we found before. o Since both criteria are not met, we say that the last data point is not an outlier , and we cannot justify removing it. I have 400 observations and 5 explanatory variables. Then decide whether you want to remove, change, or keep outlier values. Can you please tell which method to choose – Z score or IQR for removing outliers from a dataset. We are required to remove outliers/influential points from the data set in a model. 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