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Why would a uniform distribution have a larger standard deviation than a normal distribution?
The uniform distribution leads to the most conservative estimate of uncertainty; i.e., it gives the largest standard deviation. It also embodies the assumption that all effects on the reported value, between -a and +a, are equally likely for the particular source of uncertainty.
How do you prove there are no outliers?
Multiplying the interquartile range (IQR) by 1.5 will give us a way to determine whether a certain value is an outlier. If we subtract 1.5 x IQR from the first quartile, any data values that are less than this number are considered outliers.
Are there any outliers in normal distribution data?
Normal distribution data can have outliers. Well-known statistical techniques (for example, Grubb’s test, student’s t-test) are used to detect outliers (anomalies) in a data set under the assumption that the data is generated by a Gaussian distribution. See, for example: Grubbs, F. (1969).
Is it better to remove outliers before or after transformation?
Removal of outliers creates a normal distribution in some of my variables, and makes transformations for the other variables more effective. Therefore, it seems that removal of outliers before transformation is the better option.
What happens when there are too many outliers in a test?
On the other hand, swamping can occur when we specify too many outliers in the test. For example, if we are testing for two or more outliers when there is in fact only a single outlier, both points may be declared outliers (many tests will declare either all or none of the tested points as outliers).
When to use an upper bound on the number of outliers?
It has the limitation that the number of outliers must be specified exactly. Generalized Extreme Studentized Deviate (ESD) Test- this test requires only an upper bound on the suspected number of outliers and is the recommended test when the exact number of outliers is not known.