Before conducting a statistical test, it is important to understand exactly what statistical tests are and how they work.

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Thankfully, we have all the answers in this article, so keep on reading below to find out how statistical tests work, why you should use them, and the different types you can employ.

**What Are Statistical Tests?**

A statistical test is a tool that allows you to make quantitative judgments about a methodology or process.

In other words, they are tests that are employed when testing hypotheses and which use statistics to do so.

The objective is to figure out if there is sufficient substantiation to “dismiss” a process’ idle speculation or assumption.

The h0 is the name given to this assumption. If we wish to continue acting as if we “trust” the null hypothesis (h0) to be true, not dismissing it may be a great outcome.

Or it could be a bad result, inferring that we lack sufficient data to “confirm” anything by dismissing our null hypothesis.

**When Should A Statistical Test Be Conducted?**

Assume you’re a research scientist looking to determine whether there is a difference in stress levels between patient populations both before and after 4 months of psychotherapy.

How could you understand if your findings were substantial? Is psychotherapy effective in reducing stress levels? That’s when statistical tests can come in handy.

As we have already mentioned, statistical tests can be performed on information that has been obtained in a statistically relevant way, either by conducting an experiment or through field observations that use probabilistic sampling techniques.

To be accurate, a statistical test’s representative sample must be big enough to represent the real distribution of the individuals being tested.

To decide which statistical test to conduct, you must first confirm:

- whether or not your data conforms to certain hypotheses
- the kind of variables you are going to handle if you conduct the test

**What Are The Criteria For Using Statistical Tests?**

Prior to using statistical tests, some data criteria must be met. These are:

- Data should be normally distributed.
- Deviation homogeneity means that a comparable amount of ‘noise’ (possible experimental mistakes) can exist in each data set and across the different clusters.
- There shouldn’t be any extreme values.
- Independence means ensuring that the data collected by one respondent shouldn’t be affected or connected to data made by other respondents.

If your statistics do not match the normality and variance homogeneity hypotheses, you might be able to run a nonparametric statistical test, which makes it possible to run comparative testing without assuming anything about the distribution of the data.

If your statistics do not match the hypothesis of independence, you could conduct a structure-centered test (You might also want to check out What Is A Quiz?).

**Variable Categories**

The kind of statistical tests you can conduct is typically determined by the variables you have.

A quantitative variable reflects a quantity of something, like, for example, the number of cars in a parking lot. The following are some examples of quantitative variables:

- Continuous (also known as ratio variables): reflect metrics that can typically be broken down into units that are smaller than 1, like, for example, 0.50 grams.
- Discrete (also known as integer variables): depict statistics that cannot be split into units smaller than 1, like, for example, 1 dog.
- Categorical variables: these reflect groups of objects, like the different dog breeds in a dog park. Categorical variables can be of the following types:

- Ordinal: data is represented with a sequence (e.g. ranking).
- Nominal: used to reflect grouping names (e.g. product lines or breed names).
- Binary: represent yes/no or 1/0 results.

Select the test that best describes the kinds of outcome and prediction variables you’ve gathered.

**Which Parametric Test To Use?**

**Choosing Between Regression, Comparison, And Correlation Tests**

Parametric tests typically have tighter restrictions than nonparametric tests and can draw greater conclusions from data. They should only be performed with information that conforms to statistical test hypotheses.

Regression, comparison, and correlation tests are the most used types of parametric tests.

**Regression Tests**

A regression test searches for cause-and-effect connections. They can be employed to calculate the impact of one or more consistent variables on some other.

**Comparison Tests**

Comparison tests stare for variations in the mean values between groups. They can be employed to see how a categorical variable can affect the average value of another feature.

When trying to compare the averages of two groupings, T-tests are employed (e.g. the average length of cars). When contrasting the averages of many groupings, the ANOVA and MANOVA tests are employed (e.g. the average length of cars, motorcycles, and buses).

**Correlation Tests**

A correlation test determines whether variables are linked without assuming a cause-and-effect connection.

Such a test can be conducted to figure out whether two variables that you would like to include in a multivariate regression test present an autocorrelation.

**Which Nonparametric Test To Use?**

When a study’s data obtained doesn’t meet every single assumption, nonparametric tests may be used to investigate the variables.

Because nonparametric tests are less stringent, the inferences drawn from them are not as solid as those drawn from parametric tests. So:

- If both of the study’s variables are ordinal, the Spearman test can be employed instead of correlation or regression tests.
- Sign tests can be used in cases where the independent variables are categorical, and the dependent ones are quantitative.
- If there are three or more categorical independent variables and one quantitative dependent variable, the Kruskal-Wallis test is used to produce accurate results.
- If there are at least three independent categorical variables and at least two dependent quantitative variables, the ANOSI test is the most suitable one to use.
- If there are two categorical independent variables and the dependent variable is drawn from distinct quantitative groupings, the Wilcoxon Rank-sum test can be used, whereas when we’re talking about data from the same grouping, the Wilcoxon Signed-rank is best.

**The Bottom Line**

Statistical tests are helpful when figuring out the connection between variables because they offer statistical evidence for the outcomes.

When the data obtained is scientifically accurate, statistical tests can be carried out by satisfying specific hypotheses and acknowledging the kinds of variables used in the research.

So, based on your research, choose the best type of test that will bring you the desired data on the table!

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