.

There are six broad questions which can be answered in data analysis according to an article called “What is the question?” By Jeffery T. Leek, Roger D. Peng. These questions help to frame our thinking of data science problems. Here, I propose that these questions also provide a unified framework for relating statistics to data science.

The six questions according to Jeffery Leek and Roger Peng are

**A descriptive question** seeks to summarize a characteristic from a dataset. You do not interpret the results. For example: number of fresh fruits and vegetables served in a day.

**An exploratory question** is a question where you try to find a pattern of a relationship between variables i.e. you aim to generate a hypothesis. At this stage, you do not test the hypothesis. You are merely generating a hypothesis. More generally, you could say that you are proposing a hypothesis which could hold in a new sample from the population.

**An inferential question** restates the proposed hypothesis in the form of a question that would be answered by analyzing the data. You are validating the hypothesis i.e. does the observed pattern hold beyond the data at hand. Most statistical problems are inferential problems.

**A predictive question** would be one where you predict the outcome for a specific instance.

**A causal question**: Unlike the predictive and inferential questions, causal questions relate to averages in a population i.e. how changing the average in one measurement would affect another. Causal questions apply to data in randomized trials and statistical experiments where you try to understand the cause behind an effect being observed by designing a controlled experiment and changing one factor at a time.

**A mechanistic question** asks what is the mechanism behind an observation i.e. how a change of one measurement always and exclusively leads to a deterministic behaviour in another. Mechanistic questions apply typically to engineering situations.

The six questions framework raise awareness about which question is being asked and aim to reduce the confusion in discussions and media. However, they also help to provide a single framework to co-relate statistics problems to data science.

Re the six questions:

- Descriptive and exploratory techniques are often considered together
- Predictive and Inferential questions can also be combined

So we could consider four questions:

- Exploratory
- Inferential
- Causal
- Mechanistic

Why this framework matters?

That’s because it provides questions which may not have been encountered before. Here are three examples

- Typically, the starting point of data science is ‘big data’. Most data scientists are not used to dealing with small data. When you do not have a large amount of data you need to consider a more statistics driven approach see the difference between statistics and data science big data and inferen...
- The significance of causal questions i.e. Corelation vs Causation which I discuss in Correlation does not equal causation but how exactly do you determi...
- And finally – the mechanistic question which is elaborated more in Why do some traditional engineers not trust data science

The six questions provide rigor and simplicity of analysis. I also find that these questions provide a comprehensive set of questions that link statistics to data science. They help you to think beyond the norm i.e. beyond problems that you encounter to consider all possible problems.

What is the question? By Jeffery T. Leek, Roger D. Peng

image source

- How social media can help you find jobs that aren't advertised
- Insightsoftware acquisition of Izenda targets embedded BI
- Top 20 cloud computing skills to boost your career in 2021
- Will codeless test automation work for you?
- Reap the rewards of IT/OT convergence in manufacturing
- New IoT Cybersecurity Improvement Law is a start, not a final solution
- Who belongs on a high-performance data governance team?
- Interpreted vs. compiled languages: What's the difference?
- IBM acquires MyInvenio to build its automation portfolio
- Structured vs. unstructured data: The key differences

Posted 12 April 2021

© 2021 TechTarget, Inc. Powered by

Badges | Report an Issue | Privacy Policy | Terms of Service

**Most Popular Content on DSC**

To not miss this type of content in the future, subscribe to our newsletter.

- Book: Applied Stochastic Processes
- Long-range Correlations in Time Series: Modeling, Testing, Case Study
- How to Automatically Determine the Number of Clusters in your Data
- New Machine Learning Cheat Sheet | Old one
- Confidence Intervals Without Pain - With Resampling
- Advanced Machine Learning with Basic Excel
- New Perspectives on Statistical Distributions and Deep Learning
- Fascinating New Results in the Theory of Randomness
- Fast Combinatorial Feature Selection

**Other popular resources**

- Comprehensive Repository of Data Science and ML Resources
- Statistical Concepts Explained in Simple English
- Machine Learning Concepts Explained in One Picture
- 100 Data Science Interview Questions and Answers
- Cheat Sheets | Curated Articles | Search | Jobs | Courses
- Post a Blog | Forum Questions | Books | Salaries | News

**Archives:** 2008-2014 |
2015-2016 |
2017-2019 |
Book 1 |
Book 2 |
More

**Most popular articles**

- Free Book and Resources for DSC Members
- New Perspectives on Statistical Distributions and Deep Learning
- Time series, Growth Modeling and Data Science Wizardy
- Statistical Concepts Explained in Simple English
- Machine Learning Concepts Explained in One Picture
- Comprehensive Repository of Data Science and ML Resources
- Advanced Machine Learning with Basic Excel
- Difference between ML, Data Science, AI, Deep Learning, and Statistics
- Selected Business Analytics, Data Science and ML articles
- How to Automatically Determine the Number of Clusters in your Data
- Fascinating New Results in the Theory of Randomness
- Hire a Data Scientist | Search DSC | Find a Job
- Post a Blog | Forum Questions

## You need to be a member of Data Science Central to add comments!

Join Data Science Central