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What is the process for selecting a statistical test for your data?

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Data visualization is a powerful tool for communicating insights from data, but it also requires choosing the right statistical test to support your claims. How do you decide which test to use for your data? In this article, we will guide you through the process of selecting a statistical test for your data, based on four key factors: the type of data, the number of variables, the research question, and the assumptions of the test.

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Athul Anish

The real magic isn't in generating data, it's in finding the meaning hidden within. 5+ years of experience with Data…

1 Type of data

The first factor to consider is the type of data you have. Data can be classified into different levels of measurement, such as nominal, ordinal, interval, or ratio. These levels indicate how the data values can be compared, ordered, or manipulated. For example, nominal data are categorical and cannot be ranked, such as gender or color. Ordinal data can be ranked, but the intervals between values are not equal, such as satisfaction ratings or education levels. Interval data can be ranked and have equal intervals, but no true zero point, such as temperature or IQ scores. Ratio data can be ranked, have equal intervals, and have a true zero point, such as height or weight. The type of data determines which statistical tests are appropriate, as some tests require numeric data, while others can handle categorical data.

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Athul Anish

The real magic isn't in generating data, it's in finding the meaning hidden within. 5+ years of experience with Data ?? | Data Analyst | Excel Expert | R | SQL | Power BI | Tableau | KPI & Dashboards
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Selecting the right statistical test for data can very tricky but it's crucial for drawing out meaningful insights and predictions from our dataset. Depending on what question we are to answer, the statistical tool changes. For example, if we were to compare the averages of scores of two year groups in a school, we would use a t-test whereas if we were to examine the relationship between two continuous variables, we would use a correlation coefficient and to analyse the goodness of fit of a categorical variable to a theoretical distribution, we would use a chi square test.

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Nitika Garg (she/her)

Data Science Manager @ Capgemini | GCP Certified. NLP. ML. LLM, Gen AI | ex- Publicis Sapient, ex-HCL
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Yo, wanna unlock the secrets hidden in your data? You gotta pick the right test for the job, like choosing the perfect filter for your Insta story. It's all about knowing your goals and data, and then picking the test that helps you flex your analytical skills. Here's a quick breakdown: 1. What's your goal? Are you comparing groups, seeing if things are related, or predicting future trends? 2. Know your data: Is it all numbers or words? Does it follow a normal pattern or is it all over the place? 3. Design matters: Did you compare two groups or just analyze one? Are you looking at one or two variables? 4. Choose your weapon: Based on your goals, data type, and design, you gotta choose the right test.

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Sanghamitra Matta

Data Strategist | Driving Business Growth Through Data-Driven Insights | Data Analytics | Data Engineering | AI
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To select a statistical test for based on data, we could consider the following points: 1. The type of variables - Categorical or numerical. 2. The type of data distribution - Normal or Skewed distribution In addition to above points, the amount of data is also deciding factor in selecting the statistical test.

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2 Number of variables

The second factor to consider is the number of variables you want to analyze. Variables are the characteristics or attributes that can vary among the units of analysis, such as individuals, groups, or events. For example, age, income, or blood pressure are variables. The number of variables affects the choice of statistical test, as some tests are designed to compare one variable across different groups or conditions, while others are designed to examine the relationship between two or more variables. For example, a t-test can compare the mean of one variable between two groups, while a correlation can measure the strength and direction of the linear relationship between two variables.

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Sanghamitra Matta

Data Strategist | Driving Business Growth Through Data-Driven Insights | Data Analytics | Data Engineering | AI
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The number of variable dictate the type of analysis like univariate, bivariate and multivariate analysis. The more variables you include, the more complex the analysis, often requiring more advanced statistical techniques and software capabilities. With multiple variables, we have to consider interaction effects (the multicollinearity).

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3 Research question

The third factor to consider is the research question you want to answer with your data. The research question defines the purpose and scope of your analysis, and guides the selection of the most relevant and informative statistical test. The research question should specify the variables of interest, the population or sample to be studied, and the type of inference or hypothesis to be tested. For example, a research question could be: "Is there a difference in the average salary between male and female employees in a company?" This question specifies the variables (salary and gender), the population (employees in a company), and the inference (difference in means).

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Sanghamitra Matta

Data Strategist | Driving Business Growth Through Data-Driven Insights | Data Analytics | Data Engineering | AI
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Type of Research Question is important while picking the type of stat test. Type of research questions: Descriptive: Aims to describe characteristics of a variable or phenomenon (e.g., “What is the average age of participants?”). Comparative: Focuses on comparing two or more groups or conditions (e.g., “Is there a difference in test scores between students who study in groups and those who study alone?”). Relational: Investigates the relationship between variables (e.g., “Is there a correlation between exercise frequency and stress levels?”).

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4 Assumptions of the test

The fourth factor to consider is the assumptions of the test you want to use. Assumptions are the conditions that must be met for the test to be valid and reliable. Different tests have different assumptions, depending on the type of data, the number of variables, and the research question. For example, some common assumptions are normality, independence, homogeneity of variance, and linearity. If the assumptions are not met, the test results may be inaccurate or misleading, and you may need to use a different test or transform your data. Therefore, it is important to check the assumptions before performing the test and interpreting the results.

By following these four factors, you can narrow down your options and choose the best statistical test for your data. However, keep in mind that there is no definitive rule or formula for selecting a statistical test, and you may need to consult additional sources or experts to confirm your choice. Ultimately, the goal of selecting a statistical test is to support your data visualization with sound and valid evidence, and to communicate your findings clearly and effectively.

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5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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What is the process for selecting a statistical test for your data?

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1 Type of data

2 Number of variables

3 Research question

4 Assumptions of the test

5 Here’s what else to consider

Data Visualization

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What is the process for selecting a statistical test for your data?

1

2

3

4

5

1 Type of data

2 Number of variables

3 Research question

4 Assumptions of the test

5 Here’s what else to consider

Data Visualization

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