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  • Shreya Dubey

Teaching Statistics? Here’s how to make it less daunting for students

From the outside, it might look like PhD-students are only concerned with their dissertation. But, for many, PhD-life includes more than just time for own research. It also includes time for teaching.

So, PhD students face the difficult task of being students and teachers at the same time. This is not always easy, and especially not when a specific subject induces additional fear and discomfort for undergraduates: statistics!

Since such discomfort may lead to a loss of motivation for learning, it is crucial that we as teachers, make the subject less dreaded and encourage students to appreciate the role statistics plays in life within and outside university.

To help PhDs ace their role as a statistics teacher, this blogpost offers five tips to hopefully make teaching as well as learning statistics more enjoyable.

1. Explain the research process

Taking the time to explain what research is and guiding students through its various steps will lower the amount of statistical frustration they will experience later. Often, teachers start discussing the mathematical principles of statistics before referencing its applicability in research. This could be counterproductive for students because abstract mathematical concepts can be intimidating, which in turn could lead to rote memorisation of formulas without understanding the fundamentals of statistics. Hence, start your classes with an example from your field and work your way backwards.

2. Visualize

Visualisation of study hypotheses, data, and results is an important skill for presentation as well as learning. Assist students with visualising their hypotheses and results as specifically as they can before they have collected data. This can be done via figures, tables, or graphs. For instance, when comparing groups, ask students to draw a bar graph of the hypothesised effects. This will enable them to understand the hypotheses of their study better and ensure that their chosen method answers the research question.

3. Document each step

In the world of complex data and statistical software, it helps immensely to document each step of the analysis. For students, a few clicks can give the answer without much understanding of the underlying choices and mathematics. With software such as SPSS, the rule is “garbage in, garbage out.” Asking students to “show their work” when using statistical software will help deepen their understanding of statistical concepts along with their application. As a teacher, lead by example: share commented R-scripts or SPSS-syntax.

4. Practice, practice, and practice some more

Practicing with real datasets, ideally, data collected by students themselves also helps alleviate the discomfort attached with statistics courses. Ask students to start with simple things like describing the data so it makes sense. Refer them to online tutorials so that they can practice as they study along. Encourage them to note their mistakes and discuss with you or their peers. Explain that the only way to learn how to analyse data is to analyse some.

5. Show real world implications

For beginner students, statistics may not make sense out of context. To them, it might seem like a necessary step to do when writing their bachelor thesis, but how do statistics matter outside the walls of university? Show them! For instance, ask them to access publicly available data and think, for instance, about how the United Nations prepare their report on global gender equality.


Finally, here are some of my favourite resources that could help make Statistics fun for students (and therefore also for teachers – happy student, happy teacher):

1. The Art of Statistics: Learning from Data by David Spiegelhalter

2. Making Numbers Count: The Art and Science of Communicating Numbers by Chip Heath & Karla Starr

3. Discovering Statistics Using R by Andy Field & Jeremy Miles

4. Improving Your Statistical Inferences: A handy guide by Daniël Lakens for all important things stats.

5. Data is Plural: Recent datasets that might be fun to analyse with your students.


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