Category Archives: Simulation

Exploring statistics via visual simulation

As the start of the research methods module I teach is fast approaching, I have been exploring new ways to try and get students more engaged with statistics. One aspect I believe increases student understanding—and dare I say it, enjoyment—of statistics is to get their hands dirty with lots of examples.

Although it would be great to get students to go off and collect lots of different data sets, each suitable for exploring a particular statistical test, a quicker alternative is perhaps to provide the data via computational simulation. Then, students can quickly explore new data sets in interesting ways. Continue reading

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Understanding p-values via simulations

As I mentioned in an earlier post, p-values in psychological research are often misunderstood. Ask students (and academics!) what the definition of the p-value is and you will likely get many different responses. To jog your memory, the definition of the p-value is the probability of observing a test statistic as extreme—or more extreme—than the one you have observed, assuming the null is true. But, even with this definition in hand, many struggle to conceptualise what the p-value reflects. In this blog post, I take inspiration from a lovely paper I have recently read that advocates using computer simulation to understand the p-value. Continue reading

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The importance of statistical-savviness

Students often wonder why we in Psychology ask them to learn statistics. There are many good reasons, but today I just want to focus on one; surprisingly, this reason has nothing to do with Psychology. Having even just a smattering of statistical savviness can protect you from a lot of…well…bullshit. And trust me, there is a lot out there. Continue reading

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The Importance of an Adequate Sample Size

The new year is here! For me, this signals the approaching start of a year 2 research methods module that I run at Keele University. The module consists of weekly lectures and weekly lab classes, wherein students engage with classic-experiment replication and statistical analysis.

Towards the end of the semester, students break into groups of three and initiate a small research (experimental) project addressing a cognitive research question. When students get to the planning stage of their experiment, instructors always hear the same question: “How many participants do we need?”. They look at us eagerly awaiting some peal of wisdom (and a direct answer to their question), but are disappointed to hear the response: “It depends”. Continue reading

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