Table of Contents
- 1 How do you know if a non normal distribution has an outlier?
- 2 What statistical test to use when data is not normally distributed?
- 3 How do you find outliers in a normal distribution?
- 4 What is Shapiro Wilk test used for?
- 5 How do you analyze non-normal data?
- 6 Which of the following methods can be used to identify outliers?
How do you know if a non normal distribution has an outlier?
A boxplot is a nice informal way to spot outliers in your data. Usually the whiskers are set at the 5th and 95th percentile and obsevations plotted beyond the whiskers are usually considered to be possible outliers.
What statistical test to use when data is not normally distributed?
No Normality Required
| Comparison of Statistical Analysis Tools for Normally and Non-Normally Distributed Data | |
|---|---|
| Tools for Normally Distributed Data | Equivalent Tools for Non-Normally Distributed Data |
| ANOVA | Mood’s median test; Kruskal-Wallis test |
| Paired t-test | One-sample sign test |
| F-test; Bartlett’s test | Levene’s test |
Can you use a t-test for non normal data?
The t-test is invalid for small samples from non-normal distributions, but it is valid for large samples from non-normal distributions. As Michael notes below, sample size needed for the distribution of means to approximate normality depends on the degree of non-normality of the population.
How do you find outliers in a normal distribution?
Outliers. One definition of outliers is data that are more than 1.5 times the inter-quartile range before Q1 or after Q3. Since the quartiles for the standard normal distribution are +/-. 67, the IQR = 1.34, hence 1.5 times 1.34 = 2.01, and outliers are less than -2.68 or greater than 2.68.
What is Shapiro Wilk test used for?
Shapiro-Wilks Normality Test. The Shapiro-Wilks test for normality is one of three general normality tests designed to detect all departures from normality. It is comparable in power to the other two tests. The test rejects the hypothesis of normality when the p-value is less than or equal to 0.05.
Which test will you consider for a non-normal data?
A non parametric test is one that doesn’t assume the data fits a specific distribution type. Non parametric tests include the Wilcoxon signed rank test, the Mann-Whitney U Test and the Kruskal-Wallis test.
How do you analyze non-normal data?
There are two ways to go about analyzing the non-normal data. Either use the non-parametric tests, which do not assume normality or transform the data using an appropriate function, forcing it to fit normal distribution. Several tests are robust to the assumption of normality such as t-test, ANOVA, Regression and DOE.
Which of the following methods can be used to identify outliers?
Some of the most popular methods for outlier detection are: Z-Score or Extreme Value Analysis (parametric) Probabilistic and Statistical Modeling (parametric) Linear Regression Models (PCA, LMS)