The Kolmogorov-Smirnov test is one of the most widely used statistical tools for checking normality, yet research consistently shows it is often the wrong choice. Multiple peer-reviewed studies, including a highly cited 2012 paper by Ghasemi et al. published in the Journal of Applied Statistics, conclude that the KS test should no longer be used … Read more
If you have ever stared at a blank document trying to write a learning objective that actually means something, you are not alone. Most educators and instructional designers I have worked with struggle with the same issue: their objectives sound good on paper but fail when it comes time to measure them. That is exactly … Read more
Program assessment is how academic departments figure out whether students are actually meeting learning outcomes. But when a program has hundreds of students across multiple sections and courses, assessing every single piece of work becomes impractical. That is where learning how to sample student work for program assessment becomes essential. Sampling lets you draw valid … Read more
Imagine you read a headline claiming that people who carry lighters are more likely to develop lung cancer. Your first instinct might be to assume lighters cause cancer. But that conclusion misses something obvious: the people carrying lighters are smokers, and smoking is the real cause. This simple scenario captures the heart of why correlation … Read more
Imagine you are running an A/B test on two landing page designs, and your conversion data looks slightly skewed. You fire up your statistical software, run a t-test, and then freeze. Did you check normality first? If the answer is no, your results might be built on a shaky foundation. If you have ever run … Read more
Assessment alignment connects what you teach with how you measure student learning. When your tests, assignments, and tasks directly reflect your learning objectives, you get accurate data about what students actually know and can do. Many educators design lessons and then create assessments separately, which often leads to gaps between intended and measured learning. In … Read more
Every course, training program, or workshop starts with the same question: what should learners actually be able to do when it’s over? Without clear answers, instruction drifts, assessments miss the mark, and nobody knows whether learning actually happened. That is exactly why knowing how to set performance targets for learning outcomes matters so much. Performance … Read more
If you have ever stared at a scatterplot and wondered whether to run Pearson or Spearman correlation on your data, you are in the right place. The difference between Pearson and Spearman correlation comes down to one core question: is the relationship between your variables linear, or just consistently directional? Picking the wrong method can … Read more
If you run a t-test, ANOVA, or linear regression without checking one critical assumption first, your results could be completely wrong. That assumption is normality, and learning how to check whether your data is normally distributed is one of the most foundational skills in statistics. Many of the most common statistical tests assume that your … Read more
Every teacher has faced the moment after grading a test where a student asks, “Did we even cover this in class?” That uncomfortable question strikes at the heart of test validity. When assessment items drift away from what was actually taught, the scores stop meaning what we think they mean. A table of specifications (TOS) … Read more