Top 3 Statistical Tests Every Data Scientist Should Know

Learn about the three most essential statistical tests in data science — t-test, chi-square test, and ANOVA. Understand their use cases, assumptions, and real-world applications for better data-driven decisions.

Introduction

Statistical tests are crucial instruments in data science for data-driven decision-making. They assist in determining whether the patterns we observe in data are purely coincidental or significant. These tests give you a scientific basis for your conclusions, whether you're testing hypotheses, comparing user behaviour, or reviewing experimental outcomes.In this blog, we’ll explore three fundamental statistical tests every data scientist should know — the t-test, chi-square test, and ANOVA — along with when and how to use them effectively.

1. T-Test – Comparing Means Between Groups

The t-test helps determine whether the means (averages) of two groups differ statistically from one another. It's particularly helpful when determining whether a change or difference in results is not just random.

Types of T-Tests

• One-sample t-test: Compares the mean of a single group to a known value.

• Independent (two-sample) t-test: Compares means between two different groups.

• Paired t-test: Compares the means of the same group at two different times (e.g., before and after a treatment or change).

  
  

Real-World Example

Assume that a business tests Version A and Version B of its website to see which leads to longer user sessions. on average. The statistical significance of the difference in average session durations between the two groups can be ascertained using a two-sample t-test.

Key Assumptions

• Samples are independent

• Data is normally distributed

• Variances are roughly equal

Learn more: What is a T-Test?

Note: In data science, t-tests are widely used for A/B testing and model performance comparison

2. Chi-Square Test – Analysing Categorical Data

The chi-square test evaluates whether two categorical variables are related... It emphasizes counts and proportions over means.

When to Use

• To test whether two categories are related (e.g., gender vs. product preference)

• To determine whether observed data matches expected data

Types of Chi-Square Tests

• Test of independence: Checks if two categorical variables are related

• Goodness-of-fit test: Checks if a distribution of a single categorical variable fits an expected distribution

Real-World Example

A telecom business wants to investigate if the age group of its customers and their data plan choice are related. If age group has a considerable impact on plan selection, it can be determined using the chi-square test of independence.

Key Assumptions

• Both variables are categorical

• The observations are independent

• There should be at least five expected frequencies in each cell.

Also Read: Feature Selection Techniques in Machine Learning

Note: In data science, chi-square tests are essential for customer segmentation, survey analysis, and feature selection.

3. ANOVA (Analysis of Variance) – Comparing More Than Two Groups

The ANOVA test is the preferred technique when comparing the means of more than two groups. It indicates whether at least one group differs from the others in a meaningful way, but it doesn't say which one.

Types of ANOVA

• One-way ANOVA: Examines one independent variable's means across groups.

• Two-way ANOVA: Analyses the simultaneous impact of two independent variables and how they interact.

Real-World Example

Three distinct business locations' customer satisfaction ratings are examined by a data scientist. They can determine whether satisfaction differs noticeably between places by using a one-way ANOVA.

What If There Is a Difference?

To determine which groups, differ, use post-hoc tests (such as Tukey's HSD) if an ANOVA reveals a significant difference.

Key Assumptions

• Data is normally distributed

• Observations are independent

• Variances among groups are homogeneous

Note: ANOVA is powerful for experiments, marketing analysis, and testing multiple versions of a product or campaign.

Why These Tests Matter in Data Science

These statistical tests are foundational for:

• A/B testing for product or marketing strategies

• Customer segmentation analysis

• Experimental analysis in product design or usability testing

• Survey data interpretation and feedback analysis

• Model Evaluation and hypothesis testing

They allow you to avoid bias, validate hypotheses, and bring scientific rigor into business decision-making.

Conclusion

Statistical tests provide you with the assurance that your conclusions are supported by data, whether you're examining the impact of a new feature, examining consumer behaviour, or verifying marketing trials.Three essential techniques that every data scientist should know how to use are the t-test, chi-square test, and ANOVA. Gaining proficiency in these enhances not just your analytical abilities but also your capacity for effective and clear insight communication.Do you want to put these ideas to use with actual datasets in Power BI, Python, or Excel?Enrol in our extensive course on statistics and data science right now. Develop projects, get real experience, and solidify your understanding of data science.Learn with us now to develop into a self-assured data-driven professional.