Visual Statistics Tools.

Calculate and visualize the mean, median, and mode of a dataset. Compare central tendency and see how skew and outliers affect each measure.

Calculate standard deviation and variance while visualizing how data values spread around the mean. Compare sample and population calculations.

Create box-and-whisker plots showing median, quartiles, IQR, and potential outliers.

Find and visualize the minimum, Q1, median, Q3, and maximum for a dataset.

Detect potential outliers using the IQR method and Tukey fences.

Build a frequency histogram from raw data and explore distribution shape.

Generate grouped frequency tables with class intervals, frequency, relative frequency, cumulative frequency, and percentages.

Create single or back-to-back stem-and-leaf plots from a dataset.

Explore distribution skew and tail behavior, and calculate skewness and kurtosis.

Generate one or more random numbers within chosen limits, with duplicate controls and copy or download options.

Inspect a CSV dataset with descriptive statistics, charts, and outlier exploration in one browser workspace.

Select names or items at random from a supplied list.

Create two-set and three-set Venn diagrams and explore intersections and unions.

Create vertical or horizontal bar charts for category comparisons.

Plot paired X–Y data, optionally add a least-squares line, and view Pearson correlation.

Create pie or donut charts for proportions and category breakdowns.

Plot time-series or trend data with labeled axes and multiple series.

Create pictographs with icons to compare category values.

Compare multiple variables across datasets using a radar chart.

Display numerical values as a color-coded grid to reveal patterns and clusters.

Build cumulative-frequency ogives from grouped data.

Compare medians, spread, quartiles, and outliers across datasets.

Create descending bars with a cumulative percentage line.

Show numerical frequency, clusters, gaps, and possible outliers with a dot plot.

Calculate Wilson score intervals for proportions and compare them with Wald and Clopper–Pearson intervals.

Explore p-values, statistical significance, and shaded rejection regions.

Visualize one- and two-tailed rejection regions on normal or t distributions.

Explore false positives, false negatives, significance level, and statistical power.

Explore a normal curve by adjusting its mean and standard deviation.

Explore how degrees of freedom affect chi-square curves, critical values, and p-values.

Calculate left-tail, right-tail, between, or two-tail normal probabilities.

Graph a normal distribution by setting μ and σ and shading areas under the curve.

Compare t distributions with different degrees of freedom and the standard normal curve.

Draw repeated samples and compare sample means with theoretical standard error.

Visualize the share of outcome variation explained by a regression model.

Generate residual plots to examine patterns, outliers, and model assumptions.

Plot paired data with a least-squares line, equation, R², and Pearson r.

Build multi-stage probability trees with branch and path probabilities.

Simulate confidence intervals to show how often intervals capture a fixed population value.

Convert a z-score to a percentile rank, or a percentile to its z-score.