Choosing the Right Test

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Once you have collected the data, you can’t just randomly begin with any hypothesis testing tool in mind. It is important to choose the test that is most suitable and appropriate to your quantitative data. And so the next big challenge lies in the choice of the right significance test for data analysis chapter.

The three major aspects involved in the selection of the appropriate statistical test are:

1. Type of Data 2. Number of Variables being measured and 3. Number of Groups involved

To begin with, give a careful look to your gathered data and judge its type. In other words, your data would, in most cases, involve both words and numbers, within which you have to decide as to how you wish to use this data. If you find interest in categorization and proportion of your samples, consider the data as categorical. On the other hand, if your interest seeks you to work on the averages & correlation of numbers, then treat the data as quantitative. Then, the next task is to measure the number of variables in your collected data. Conducting a significance test needs the knowledge of the number of variables since there is a separate test for one variable, two variables and multiple variables of data respectively. Also, focus on the specific question that the test is answering.

The third important consideration is the number of groups being studied in your research. There are single sample studies and comparative studies (that describe the differentiation of one group to the other), among which you can decide the one suitable to your data. Now, you can formulate a hypothesis that is a clean declarative statement, which will further help you in the choice of the right test.

To ease your process, we have also presented you a flow diagram which will enable you in choosing the helpful type of test.

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