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How Statistics & Experimentation are Tested

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Statistics is a critical skill for accurate and insightful data analysis. Whether you're measuring business performance, running A/B tests, or making predictions, statistical thinking allows you to:

  • Understand data characteristics before data manipulation and analysis.
  • Extract meaningful insights and identify trends, anomalies, and patterns.
  • Ensure data accuracy and avoid misleading conclusions that can impact decision-making.
  • Support business strategy with data-driven, well-founded statistical models.

Without a strong foundation in statistics, analysts risk making fundamental errors—such as misinterpreting correlations, ignoring sample size effects, or misusing averages—that can lead to bad business decisions with real financial consequences.

In big tech companies, statistics is not assessed in a separate interview round for data analytics roles. Instead, statistical thinking is embedded across different parts of the interview process.

Here’s how hiring managers may test your statistical knowledge:

RoundExampleWhat they’re testing
Data interpretation questions & case study roundsYou're given a dataset with monthly ad spend and customer acquisition numbers. You notice that in some months, ad spend increased, but customer acquisition didn't. How would you investigate this?Your understanding of statistical correlation vs. causation and ability to think critically about data relationships.
Product sense or A/B testing discussionsAn A/B test results in a p-value of 0.04. What does this mean, and how should the business interpret the result?Your understanding of statistical significance, confidence levels, and real-world decision-making based on experiment results.
Conceptual questions to test your understandingHow do you identify and handle outliers in a dataset?Your fundamental knowledge of statistics and ability to explain concepts clearly, both technically and to non-technical stakeholders.

How to use this course

If your target role involves experimentation, A/B testing, or advanced statistical modeling, you should invest time in advanced topics like power analysis and hypothesis testing.

For most data analytics roles, mastering foundational topics will be sufficient.

Core foundational statistics topics (must-know for all data analysts)

These are the fundamental concepts that every data analyst must master:

Advanced statistics & experimentation

If you would like to dive deeper into a particular topic, visit our Statistics & Experimentation course for data scientists.

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