We all have our strengths—and our areas for growth. When I first started teaching psychology, statistics was definitely an area I needed to grow in. Mean, median, and mode? I could handle those. But some of the more complex statistical concepts took a little more work. And I knew I wasn’t alone. Many of my students approached statistics with the same hesitation. So I started asking myself a question I come back to often:
How can I make this meaningful—and maybe even a little fun?
If I’m not enjoying what I’m teaching, my students probably aren’t enjoying it either.
Making Statistics Meaningful
I introduce statistics as part of our research unit. Before students can interpret research, they need to understand where data come from and why the way we collect them matters.
How do psychologists collect information that gives us a meaningful picture of human behavior?
That opens the door to conversations about sampling, representative data, sample size, measures of central tendency, variability, and all of those statistical concepts that can feel pretty abstract when they exist only on a slide or in a textbook. I wanted students to actually see those ideas in action.
Enter the Skittles.
Do you remember going to the dentist’s office, a school carnival, or maybe a local business as a kid and seeing one of those giant jars filled with candy? There was always something strangely irresistible about it: guess the number, and you might win the whole thing.
While looking for a way to make statistics more engaging, I stumbled across the NOVA video Prediction by the Numbers, and something clicked. The video begins with a game. People guessed how many candies were in the jar. After collecting the guesses, the statistician showed how the group mean was much more accurate than individual guesses.
I loved the idea of using individual guesses to show students what can happen when we combine information from a larger group. So, naturally, I bought a lot of Skittles. I poured them into a clear bag and presented it to my students and gave them one simple challenge:
How many Skittles are in the bag?
The closest answer won the entire bag. And suddenly, statistics became very serious business. Students counted visible Skittles. They examined the bag’s dimensions. They estimated volume. They debated strategies with the people around them. Some relied on math, some relied on instinct, and some appeared to be developing methods known only to them. But they were all invested.
Each student entered a guess in a Google Form so I could compile responses from all my classes. And then we looked at the data.
What Did We Find?
There were 756 Skittles in the bag. Across all of my classes, 111 students submitted guesses ranging from 72 to 1,559. That is quite a range. Students could immediately begin applying what we had learned. We could calculate the mean, median, mode, and range. We could look for outliers. We could compare class periods. We could examine the shape of the distribution.
But the most interesting part wasn’t simply calculating the statistics. It was asking what those statistics could tell us.
The Wisdom of Crowds
Individually, our estimates were all over the place. Some students dramatically underestimated the number of Skittles, while others dramatically overestimated it. But when we combined all 111 guesses, the extreme estimates began to balance one another out. The group average was much more stable than many of the individual guesses.
This is an example of aggregation, sometimes called the wisdom of crowds. When people make reasonably independent estimates—and their errors are not all biased in the same direction—combining those estimates can produce a surprisingly useful result.
It also gave us an intuitive way to introduce the Law of Large Numbers. Formally, the law tells us that as we collect more independent observations, an average tends to stabilize and move closer to the expected value. Our activity wasn’t a perfect textbook demonstration because we weren’t repeatedly drawing random observations from a population. Still, students could see the underlying idea: an average based on many observations is less influenced by one unusual response than an average based on only a few.
That distinction made the lesson richer because more data do not automatically mean better data. If everyone uses the same flawed strategy—or if the participants are systematically biased in the same direction—adding responses simply creates a larger collection of biased information. Similarly, a psychological study with 10,000 participants can still be misleading if those participants do not represent the population researchers want to understand.
The lesson, then, is that larger samples are not always better. Students begin to see that psychologists must consider both how much data they collect and where it comes from. Sample size, independence, and representative sampling all shape the conclusions we can reasonably draw.
That is much more meaningful than memorizing a definition—and considerably easier to understand when 756 Skittles are sitting in front of you.
Learning in Action
This wasn’t a complicated lesson. It required a bag of candy, a Google Form, and one question. But the low-stakes competition gave students a concrete data set they genuinely wanted to analyze. They weren’t just calculating statistics; they were beginning to understand why those statistics mattered.
I don’t need every student to leave psychology loving statistics—I’m still working on that myself. But I do want them to understand that statistics help us make sense of the world and that sometimes the best way to understand an idea is to see it happen right in front of you.
Preferably with Skittles.
These changes preserve your central ideas while removing roughly five paragraphs’ worth of repetition. The strongest throughline becomes clearer: an engaging guessing game leads to calculation, aggregation, the Law of Large Numbers, and finally the limitations of large samples.
Be well,
Cori
NOTE: I received several requests for a lesson write-up and/or student handout. Here you go!
Blended Learning Journey 
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