The Art of Asking Statistics Questions: Who, What, Where, When, Why, and How

Have you ever wondered how researchers come up with meaningful statistics that provide insights about the world around us? It all starts with asking good statistical questions. Statistics helps us understand variability in data and make inferences about populations. But asking effective statistical questions is an art that takes practice. In this blog post, we’ll cover tips for identifying your target population, choosing variables, picking data collection methods, and articulating why your statistical question matters. Follow along to level up your statistical questioning skills!


Statistics is all around us, from news stories reporting the latest polling data to research studies exploring the effects of medications. Behind every statistic is an important statistical question. But not every statistical question yields meaningful results. Crafting statistical questions that lead to impactful data requires thoughtfulness and care.

Let’s break down the key considerations when developing statistical questions using the classic journalism questions: Who, What, Where, When, Why, and How. Keeping these questions in mind will set you up to gather significant statistics that provide real insights.

Who Should You Ask?

Identifying the appropriate target population is crucial when developing your statistical question. Your population determines the data you will be able to access and the inferences you can make from your statistics.

If your question is about teenagers’ smartphone use, for example, surveying senior citizens won’t get you very far. Carefully define the segment of people or objects you want to study based on characteristics like demographics, behaviors, locations, and more. Get clear on inclusion and exclusion criteria when choosing your target population.

You also need a sampling method to get representative data from your population. Simple random sampling gives everyone equal chances of being selected. Stratified sampling groups your population into homogenous segments and takes samples from each one. Cluster sampling selects clusters or groups, then samples from within them. Match your sampling technique to your population and research goals to avoid biased data.

Clearly describing your target population and sampling method allows readers to understand who your statistics apply to. Don’t collect data from the wrong group or use biased sampling and undermine your statistical findings before you even begin.

What Should You Measure?

Now that you’ve identified your population, the next step is choosing what variables to measure. Your variables need to align with your statistical question to produce meaningful data.

Carefully define each variable you plan to collect data on. If your question involves smartphone use, your variables might include:

  • Time spent on phone per day
  • Primary smartphone activities
  • Emotions associated with phone use

Consider how variables relate to each other. Time spent on phones and enjoyment of phone activities could be interesting to analyze together. Variety of phone activities and demographic factors like age may also have relationships to uncover.

Choose variables that help answer your research question within your target population. Don’t just collect data on variables because they are easy to measure. Every variable should have a purpose.

Clearly explaining your key variables helps readers understand what data your statistics describe. Well-defined variables also ensure your data collection stays focused on gathering useful information.

Where Should You Collect Data?

Now that you know who you want to study and what variables to measure, the next consideration is where to collect your data. Data collection locations must provide access to your target population and allow you to gather information on your key variables.

If your question involves studying teenagers’ smartphone use, for example, schools provide convenient access to this demographic. But other options like malls, parks, or social media platforms may enable you to observe natural phone use instead of self-reported data.

Consider the pros and cons of field locations where you can directly interact with your target population versus existing secondary datasets. Field data collection allows control and observation but requires significant effort. Existing datasets are more accessible but limit your variables and populations.

Where you choose to collect data impacts available populations, variables, collection methods, and ultimately the inferences you can draw from your statistics. Pick locations that align with your research goals and question.

When Should You Collect Data?

The timing of data collection is another key consideration when developing your statistical question. Time periods should be chosen intentionally based on your research aims rather than convenience.

If your goal is to analyze changes over time, collect data at multiple meaningful time points. Comparing smartphone use at the beginning and end of a school semester could provide insights. For questions involving ongoing behaviors, periodic data collection such as monthly surveys may be appropriate.

Also consider external events that could influence your data. Holidays, new product releases, or school breaks can affect behaviors. Aim to collect data during typical periods to get accurate insights into regular behavior patterns.

Choosing the right data collection time periods allows you to observe meaningful changes and trends. Don’t just collect data once at an arbitrary or easy time. Thoughtful timing gets you the data you need.

Why Does Your Question Matter?

With careful thinking about population, variables, locations, and timing, you can develop statistically sound questions. But why does your particular statistical question deserve attention in the first place? What makes it worth investigating and important to answer?

Take a step back to articulate why your research question and resulting data will be meaningful. What new insights will your statistics provide? How will quantifying these patterns and trends add value?

For example, studying how much time teenagers spend on smartphones could:

  • Uncover relationships between phone use, sleep, and mental health
  • Inform policies around phone use in schools
  • Allow parents to compare their child’s habits to peers

Being able to explain why your statistical question matters provides focus for your research. It also allows you to anticipate how your data can be used and applied in meaningful ways.

How Will You Collect Your Data?

Finally, once you’ve determined what you want to study and why it is important, the final consideration is how to collect your data. Quantitative and qualitative methods both provide value.

Quantitative methods like surveys and structured observations produce numerical data. This data allows statistical analysis to identify patterns, test theories, and make data-driven decisions. Interviews, focus groups, and open-ended observations provide qualitative data that adds context about people’s experiences and perspectives.

When choosing data collection methods, match them to your research aims, target population, and available resources. Using multiple methods often provides the most complete picture by combining hard numbers with stories and insights.

Key Takeaways

Developing statistical questions that yield meaningful data is an iterative process. Keep these tips in mind as you craft your next research question:

  • Clearly define your target population and sampling method
  • Choose key variables that align with your aims
  • Identify data collection locations that fit your population and variables
  • Pick intentional time periods to observe changes over time
  • Articulate why your question is worth investigating
  • Use quantitative and qualitative methods that fit your needs

Asking good statistical questions takes practice. But the effort pays off through data that provides significant insights and value. By thinking through who, what, where, when why, and how, you’ll be on your way to statistics that matter.