Outlier removal is common in hormonal research. Here we investigated to what extent removing outliers in hormonal data leads to divergent statistical conclusions. We first show that the most common outlier detection rule is based on a number of standard deviations (SD) from the mean. Next, we used simulations to examine the degree to which statistical conclusions diverge when a test with. Indeed, outliers are typically computed using the rule commonly known as the "1.5 times IQR" rule. Also, sometimes outliers are computed using z-scores, where any raw score with a z-score that has an absolute absolute greater than 2 is an outlier. Example: Outlier Detection.
First, choose the significance level (alpha) where an outlier will be detected. The most common choice is .05. Then copy and paste your data into the right side. Be sure to enter one data point on each line. Do not use a long list separated by commas!.
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SPSS Statistics Output. SPSS Statistics outputs many table and graphs with this procedure. One of the reasons for this is that the Explore... command is not used solely for the testing of normality, but in describing data in many different ways. When testing for normality, we are mainly interested in the Tests of Normality table and the Normal Q-Q Plots, our numerical and graphical methods to.
Using the new file, redefine the levels of your variable so that your outliers are excluded - the TRANSFORM command will do this. Then redo you boxplots and you should be good to go Hope this helps Kannan Rangaswami More than 15 years in teaching data analysis. Author has 297 answers and 165.5K answer views 3 y.