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Should you include outliers in results?
Removing outliers is legitimate only for specific reasons. Outliers can be very informative about the subject-area and data collection process. Outliers increase the variability in your data, which decreases statistical power. Consequently, excluding outliers can cause your results to become statistically significant.
When should an outlier not be removed?
Examine an outlier further if: If the outlier creates a relationship where there isn’t one otherwise, either delete the outlier or don’t use those results. In general, an outlier shouldn’t be the basis for your results.
What happens when you remove outliers from a data set?
By removing outliers, you’ve explicitly decided that those values should not affect the results, which includes the process of estimating missing values. Both cases suggest removing outliers first, but it’s more critical if you’re estimating the values of missing data.
When to drop an outlier in a paper?
If the outlier does not change the results but does affect assumptions, you may drop the outlier. But note that in a footnote of your paper. More commonly, the outlier affects both results and assumptions. In this situation, it is not legitimate to simply drop the outlier.
When do outliers need to be sensitive to?
Outliers are one of those statistical issues that everyone knows about, but most people aren’t sure how to deal with. Most parametric statistics, like means, standard deviations, and correlations, and every statistic based on these, are highly sensitive to outliers.
How are scatterplots used to show outliers?
Scatterplots show a collection of data points, where the x-axis (horizontal) represents the independent variable and the y-axis (vertical) represents the dependent variable. Scatterplots can easily show the “12-year-old widow” from in the example above as an outlier separate from the rest of the grouped data points.