7/29/2019 Tips to Choose the Right Statistical

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Tips to Choose the Right Statistical Test for Your Research

With a wide range of statistical tests available to analyze and interpret your research data, it

becomes difficult to choose the best-suited test for your study design. Though it is a tricky situation

to find and select the best test for comparing measurements, you may still follow certain tips while

making your choice. First, you might like to consider if a parametric or nonparametric family of tests

would suit the design of your study. While parametric tests are based on the assumption that data

sampling is done from a Gaussian distribution, nonparametric tests are not based on assumptions

related to population distribution.

Some common examples of parametric tests are T test, one-way ANOVA, and linear and nonlinear

regression. Nonparametric tests examples include Wilcoxon test, Mann-Whitney test, Kruskal-Wallis

test, Friedman test and Spearman correlation. Thus, it is an easy choice to go for parametric tests if

your data sampling is done from a population following a Gaussian distribution. A nonparametric

test can be selected if your population does not follow a Gaussian distribution or your outcome is a

score or rank. It can also be selected when some values are too low or too high to measure.

Sometimes, it is not easy to check whether a sample comes from a population following a Gaussian

distribution or not. In such a case, you should look at the overall data and not just the data for the

current experiment. If there are multiple sources causing scatter, then it could be a case of Gaussian

distribution. However, if you are in doubt, go by your best judgment for selecting a parametric or

nonparametric test. Another aspect that you should consider while selecting a test is your sample

size. Large samples would generally cause no issues if you use any of the above-mentioned tests.

Thus, take care while choosing the test for a small sample size.

Further, you should choose a test after deciding to calculate a P value that is either one-sided or

two-sided. Go for a one-sided test before collecting any data and after deciding the direction of your

hypothesis. If you are curious about the data that goes another way, opt for a two-sided test. Apart

from this, consider if your values represent measurements on matched subjects or repeated

measurements on a single subject. If any of these is the case, you should prefer a paired or repeated

measures test. However, compare the groups using an unpaired test if individual values are not

matched with one another.

Thus, these are some of the tips that might help you select the right test for your study. If you areunsure of various statistical procedures and intricacies, you might opt to take help from the expert

statisticians working with various companies like www.statworkz.com that provide dissertation

statistics and data analysis support.

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