10 Basic Statistics Review MicroSims
Here’s a scaffolded set of Ten Basic Statistics MicroSims designed for students entering your data science course without a strong statistics background. I’ve ordered them from simplest to more complex, drawing from the MicroSim styles already in your library.
1. Exploring Data Points
Description
Students click to add or remove points on a 2D scatter plot, instantly seeing the effect on the dataset.
Learning Goals
- Recognize individual data points as observations.
- Understand how points are represented in x–y coordinates.
- See how changes in points affect the shape of a dataset.
Input Controls
- Add Point (click on canvas)
- Remove Point (click existing point)
- Clear All Points (button)
2. Histogram Builder
Description
Students adjust bin sizes to see how histograms change, revealing over-smoothing or excessive detail.
Learning Goals
- Understand bins and frequency counts.
- Relate bin size to detail retention in data.
- Connect histograms to raw data.
Input Controls
- Bin Size Slider
- Dataset Selector (normal, uniform, skewed)
- Toggle Grid Lines (checkbox)
3. Mean and Median Explorer
Description
Drag points along a number line to see how the mean and median respond differently to changes.
Learning Goals
- Differentiate mean vs. median.
- Understand how outliers affect each measure.
- See why median is more robust.
Input Controls
- Drag Points (mouse)
- Add Outlier (button)
- Reset Points (button)
4. Distribution Shape Explorer
Description
Morph between uniform, normal, skewed, and bimodal distributions and see parameter changes in real time.
Learning Goals
- Identify common distribution shapes.
- Understand skewness and kurtosis.
- Connect distribution properties to data characteristics.
Input Controls
- Distribution Type Selector
- Skewness Slider
- Kurtosis Slider
5. Correlation Playground
Description
Students drag clusters of points to change correlation and watch the correlation coefficient update.
Learning Goals
- Visualize correlation strength and direction.
- Understand positive, negative, and zero correlation.
- See the impact of noise on correlation.
Input Controls
- Drag Cluster (mouse)
- Add Noise (slider)
- Show Best Fit Line (toggle)
6. Least Squares Line Fitter
Description
Adjust slope and intercept to minimize squared residuals with visual feedback.
Learning Goals
- Understand slope, intercept, and residuals.
- Experience manual fitting before automation.
- Build intuition for regression.
Input Controls
- Slope Slider
- Intercept Slider
- Toggle Residual Squares (checkbox)
7. R² Intuition Builder
Description
Manipulate data spread around a regression line and see how R² changes.
Learning Goals
- Understand what R² measures.
- Relate R² to model fit.
- Recognize limits of R² as a single metric.
Input Controls
- Noise Level Slider
- Number of Points Slider
- Reset Dataset (button)
8. Sampling Bias Demonstrator
Description
Draw samples from representative or biased datasets to see effects on mean/median estimates.
Learning Goals
- Recognize sampling bias.
- Connect bias to flawed conclusions.
- Practice identifying representative samples.
Input Controls
- Sampling Method Selector (random, biased)
- Sample Size Slider
- Reset Data (button)
9. Hypothesis Testing Visualizer
Description
Adjust sample statistics to see how p-values change under different population means.
Learning Goals
- Understand null and alternative hypotheses.
- Interpret p-values visually.
- See the effect of sample size on significance.
Input Controls
- Population Mean Slider
- Sample Mean Slider
- Sample Size Slider
10. Confidence Interval Explorer
Description
Show multiple sample means with confidence intervals to explore coverage probability.
Learning Goals
- Understand confidence intervals conceptually.
- Connect sample size to interval width.
- Interpret intervals in the context of repeated sampling.
Input Controls
- Confidence Level Slider
- Sample Size Slider
- Number of Samples Slider