**Choosing the correct statistical test depends on three basic things:**

**The type/kind of data being analyzed.**- Are the data categorical or continuous?

**The number of samples in the study.**- One sample, two samples, or 3 or more samples?

**The type of research question / hypothesis.**- Are you looking for a difference or comparison between variables or something else like a relationship?

- Nominal
- Sex, blood, type, color, dog breed, affiliations...

- Ordinal
- Class range, age group, educational level, income level..

Categorical data are basically word or label answers.

Summary data is usually stated as frequencies, proportions, rates, or percentages.

EX: Do you have siblings? Yes/No

*Don’t be trick because you count the number of yes and no responses for example. You count to get a proportion that said yes and the proportion that said no. The data itself is not a count.*

**Chi Square**- A chi-square test is used when you want to see if there is a relationship between two categorical variables.

**Fisher's Exact Test**- The Fisher’s exact test is used when you want to conduct a chi-square test but one or more of your cells has an expected frequency of five or less.

**McNemar**- Use if you were interested in the marginal frequencies of two binary outcomes.

**Mann-Whitney**- Is the non-parametric alternative test to the independent sample t-test.
- Used to compare two sample means that come from the same population.
- Used when the data is ordinal or when the assumptions of the t-test are not met.

**Kruskal-Wallis**- Used when you have one independent variable with two or more levels and an ordinal dependent variable.
- It is the non-parametric version of ANOVA.

**Spearman's Rank Correlation**- Used to test the association between two ranked variables, or one ranked variable and one measurement variable.

**Wilcoxon Rank sum Test**- Used when you don't assume that the difference between the two variables is interval and normally distributed but you do assume the difference is ordinal.
- Non-parametric version of a paired samples t-test

- Interval
- IQ score, temperature...

- Ratio
- Weight, height, pulse rate...

Continuous Data are also called quantitative date and are expressed in numbers.

Examples include temperature, blood pressure, pain scale. Counts are also included such as number of hospital stays or number of adverse events.

Summary data is expressed as a mean.

EX: How many siblings do you have?

**Single sample t-test**- Compares a sample mean to a known population mean.
- EX: Comparing ACOM students' average MCAT scores to the national average MCAT score.

**Independent samples t-test (unpaired)**- Compares two distinct samples.
- EX: Comparing ACOM students' average MCAT score to the average of another school.

**Dependent samples t-test (paired)**- EX: Comparing the average test score before pet therapy and then after pet therapy.
- Other examples: before and after treatment of a patient or comparing twins.

**t-tests vs. z-test**

In general, use the t-test if you DO NOT know what the standard deviation is and z-test when you do know the standard deviation. In general, this mean t-tests is used for small samples and the z-test for large ones.

You use ANOVA (analysis of variance) when there are three or more samples you want to analyze at once.

Two types of ANOVA:

**One Way ANOVA**- Compares three or more unmatched groups.

**Two Way ANOVA**- Determines how a response is affected by two factors.

Study design and choosing a statistical test | The BMJ. (n.d.). https://www.bmj.com/about-bmj/resources-readers/publications/statistics-square-one/13-study-design-and-choosing-statisti

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De Muth, James E. “Overview of Biostatistics Used in Clinical Research.” American Journal of Health-System Pharmacy 66, no. 1 (January 1, 2009): 70–81. https://doi.org/10.2146/ajhp070006.

Erich Goldstein. (n.d.). Choosing a Statistical Test. Retrieved from https://www.youtube.com/watch?v=UaptUhOushw

Parab, S., & Bhalerao, S. (2010a). Choosing statistical test. International Journal of Ayurveda Research, 1(3), 187–191. https://doi.org/10.4103/0974-7788.72494

Shankar, S., & Singh, R. (2014). Demystifying statistics: How to choose a statistical test? Indian Journal of Rheumatology, 9(2), 77–81. https://doi.org/10.1016/j.injr.2014.04.002

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