Stats Without Walls User Guide

Stats Without Walls is a free statistical tool for all levels. It runs entirely in your browser: nothing you load is uploaded anywhere, there is no account, and it never costs anything. This guide explains every menu, what each option does, and how to read what comes back.

1. Start here

Three ways to run it

1. Web. Open learnwithoutwalls.com/stats in any modern browser (Chrome, Edge, Safari, Firefox). This version always has the newest features.
2. Install from the browser. In Chrome or Edge, click the install icon in the address bar (or Chrome menu, then "Install Stats Without Walls"). It opens in its own window and works offline after the first visit.
3. Desktop app. For now the browser version is the better choice: the app is updated almost daily and the desktop app cannot update itself yet (a self-updating version is coming). If you need it, download the Mac or Windows installer from the download page. Mac: pick Apple Silicon (M1 to M4) or Intel. The first time you open it on a Mac, right-click the app and choose Open, because it is not signed with an Apple developer certificate. If your Mac says the app is damaged and offers no Open Anyway button, see Troubleshooting for the one-line fix.

Privacy

Your data stays on your computer. The app has no server. If you close the tab, the last dataset and your results are remembered by your browser (local storage) so you can pick up where you left off; clearing your browser data removes them. Use Results, Save session to keep a permanent copy.

A two-minute first run

1. Data, Sample datasets, pick "Our class data (40 students)", click Open.
2. Exploration, Descriptives, select GPA, click Run. A card appears in the Analyses tab.
3. Graph, Histogram, choose Hours_Sleep, click Run. The graph appears in the Graphs tab.
4. T-Tests, Independent Samples T-Test, dependent variable Hours_Sleep, grouping variable Employed, click Run. Read the blue interpretation box at the bottom of the card.

2. The screen

Variable types

The app guesses a type for each column when data loads. The type decides which menus can use the column.

If a guess is wrong (a numeric code that is really a category, or a numeric column that was read as text because one cell contains a word), click the type under the column name and pick Continuous, Ordinal, Nominal or ID, the same job as a data setup panel in other tools. A type you set by hand stays set, even after you edit cells. Data, Set variable type does the same thing from the menu.

3. Getting data in

Open CSV file

Data, Open CSV file. Any comma-separated file with one row of column names at the top. Exports from Excel, Google Sheets, Canvas, Qualtrics and survey tools all work. Quoted text with commas inside is handled. Dates stay as text.

Paste data

Data, Paste data. Copy a block from a spreadsheet (including the header row), paste it in, give it a name. Tabs and commas both work.

Sample datasets

Data, Sample datasets. Real and course datasets that ship with the app, listed in section 13. They also work offline in the installed app.

New data table

Data, New data table. Type column names separated by commas and how many rows you want. Then click cells to type values. Good for a small in-class exercise.

Editing cells

Click any cell, type, then click away or press Tab. Types are re-guessed after each edit. Column names can be changed with Data, Rename or delete column.

Download data as CSV

Saves the current table (including computed, recoded and z-score columns) as a CSV file you can open in Excel or load again later.

4. Preparing data

Filters

Data, Filters, or the filter box above the table. Write a condition using column names exactly as written. Rows that fail the condition are greyed out and left out of every analysis and graph until the filter is cleared.

Year == "Freshman"
Commute_Minutes < 30
Year == "Senior" AND GPA >= 3.5
Major == "Biology" OR Major == "Nursing"
Employed != "Full-time"

Operators: == equal, != not equal, < <= > >=, AND, OR. Text values go in quotes. A single = is accepted as equal.

Compute a new variable

Data, Compute. Name the new column and write a formula using existing columns.

Hours_Study * 7                       weekly hours
Commute_Minutes / 60                  minutes to hours
(GPA - mean(GPA)) / sd(GPA)           z-score by hand
log(Income)                            natural log
round(Age / 10) * 10                  decade
Hours_Sleep >= 7                       gives true or false

Functions: log (natural), log10, sqrt, abs, exp, round, floor, ceil, pow(x, k). Whole-column summaries: mean(X), sd(X), median(X), min(X), max(X), sum(X), n(X). A row with a missing value in any column used gets a blank.

Transform (recode)

Data, Transform. Turn values into new values, one rule per line, as old = new. Numeric ranges are written 0 to 29 = short. "Everything else becomes" catches what the rules miss; leave it blank to keep the original value.

Full-time = Employed
Part-time = Employed
Not employed = Not employed

0 to 29 = short
30 to 59 = medium
60 to 300 = long

Add z-score column

Data, Add z-score column. Adds z_Variable with (x minus mean) divided by the sample SD, the same as the by-hand formula.

Bin into classes

Data, Bin into classes. Turns a numeric variable into classes of a width you choose ("60 to under 70"), starting where you say. Each class includes its left end. The new column is categorical, kept in numeric order, ready for a frequency table or bar plot.

Stack columns

Data, Stack columns. When each group sits in its own column, stacking makes one column of values and one column naming where each value came from, the layout two-sample tests, ANOVA and side-by-side graphs need. It makes a new data table, so download the old one first if you need it.

Simulate random data

Data, Simulate random data. Random values from a normal, uniform, discrete uniform, binomial, Poisson, exponential or yes or no distribution, any number of rows and columns (one column per sample). If the row count matches the open table the columns are added to it; otherwise a new table is made. A seed repeats the same numbers, so a whole class can work with identical data.

Random sample of rows

Data, Random sample of rows. A simple random sample from the open table, with or without replacement, with an optional seed. Uses only the rows kept by a filter.

Sorting

Click a column header. Sorting changes the row order for Time Series and for the display; analyses do not depend on it.

5. Reading a result card

Every analysis produces a card with the same layout, top to bottom:

1. Title and a grey line naming the dataset, the variables and n. Three buttons: edit (reopens the same dialog with your choices filled in; change anything and Run, and the new result replaces this card in place, the StatCrunch habit), copy text (copies the whole card as plain text for a report) and remove.
2. Tables, named the way the course instructions name them (Descriptives, Independent Samples T-Test, Model Fit Measures, Contingency Tables, and so on), so the instructions transfer as written.
3. Formula box (grey, monospace): the formula that produced the statistic, with the symbols the course uses: mu and x bar for means, sigma and s for standard deviations, p and p hat for proportions.
4. Blue box: the decision and a written conclusion in plain English, including the p-value compared with alpha.
5. Green or pink box: a condition check. Green means the condition for the method is met (for example n at least 30, or at least 10 successes and 10 failures). Pink means it is not, and says what to do instead.
6. Graphs attached to the analysis (descriptives plots, residual plots), below the text.

Decimals. Numbers are shown to the number of decimals set under Results, Decimal places shown (3 by default). Internally everything is full precision.

6. Which analysis do I need

7. Menu by menu

Exploration

Descriptives

Pick one or more variables; optionally split by a group. Tick the statistics you want: N, missing, mean, median, mode (all modes are listed when there is a tie), sum, standard deviation (sample, n minus 1), variance, population SD, range, minimum, maximum, standard error, IQR, quartiles, fences for outliers, skewness, kurtosis, coefficient of variation, and any other percentiles you list (such as 10, 90). Tick plots: histogram, density, boxplot, dotplot, QQ plot, bar plot for categorical variables. Frequency tables appear for nominal and ordinal variables, with counts, relative frequency, percent, cumulative frequency, cumulative relative frequency and cumulative percent. Classes named by numbers ("60 to under 70") are listed in numeric order.

Quartile method. "Textbook halves" (default) finds Q1 and Q3 as the medians of the lower and upper halves with the overall median excluded, which is what the course teaches by hand. "Type 7" is what R and Excel use by default; the two can differ for small n. Boxplots use the same rule as the descriptives.

Scatterplot

Quick scatterplot with an optional group colour and regression line; the Graph menu version has more options.

Graph

All graphs land in the Graphs tab. Every graph follows the honest-graph rules, and says so under the picture. Every graph dialog has an Appearance section (title, axis labels, color), and the edit button on the card reopens it with your choices filled in.

T-Tests

Every t-test dialog has a Data switch: "from the data table" or "summary statistics" (type mean, SD and n from a textbook problem). Options: mean difference with confidence interval, effect size (Cohen's d), descriptives, descriptives plots.

One Sample T-Test

Tests whether the population mean equals a test value. Reports t, df, p, mean difference, the confidence interval, and the condition check (n at least 30, or a normal-looking QQ plot when n is smaller). The formula box shows t = (x bar minus mu0) / (s / sqrt(n)).

Independent Samples T-Test

Compares two group means. Welch's version (no equal-variance assumption) is the default; untick it for Student's. Hypothesis can be two-sided, group 1 greater, or group 1 less. If the grouping variable has more than two levels, name the two you want. Summary-statistics mode takes mean, SD and n for each group.

One Sample Z-Test and Two Sample Z-Test

Only for problems that give the population standard deviation sigma. You type sigma (one, or one per group); the card gives z, the p-value, and the interval x bar plus or minus z* sigma / sqrt(n). If only the sample SD s is known, use the t-test: the card says so.

Paired Samples T-Test

Two columns measured on the same subjects (before and after). The test is a one-sample t on the differences; tick "Histogram of differences" to see them.

ANOVA

One-Way ANOVA

Three or more group means. Fisher's (equal variances) and Welch's F are both available; the homogeneity check compares the largest SD with the smallest (ratio under 2 passes), and Levene's test (deviations from the median, the Brown-Forsythe version) is available as a formal check. Tukey post-hoc tests tell which pairs differ, with adjusted p-values. Descriptives table and plot (means with confidence intervals) are optional. Two-way ANOVA and repeated measures are under Advanced.

Regression

Correlation Matrix

Two or more numeric variables. Pearson (linear) and Spearman (rank) correlations, with p-values if requested, and a grid of scatter plots. r near 0 means no linear relationship, not no relationship.

Linear Regression

One dependent variable and one explanatory variable (covariate). Reports the coefficients table (intercept, slope, SE, t, p, confidence interval), R and R squared, the residual plot and QQ plot of residuals, and predictions at one or several x values (comma separated). Each prediction comes with a confidence interval for the mean response and a prediction interval for one new value, and a warning when x is outside the data. Tick "Show confidence and prediction bands" to draw both bands around the line. The slope interpretation sentence is written for you.

Frequencies

2 Outcomes: Binomial test

One proportion against a test value. The exact binomial p-value is always given; tick "Also show the z test" for the course's by-hand method with the success and failure check (n p0 and n (1 minus p0) both at least 10). Summary mode: successes and n. Interval method: standard (Wald, the by-hand formula, default), plus four, Agresti-Coull, Wilson score, or exact Clopper-Pearson. When there are fewer than 10 successes or failures, the card points you to one of the small-sample methods instead of the standard interval.

N Outcomes: chi-square Goodness of fit

Do observed counts across categories match expected proportions (equal by default, or the ones you type)? Shows expected counts and the check that all are at least 5.

Contingency Tables: Independent Samples

Two categorical variables, or a typed table of counts. Chi-square test of independence with expected counts, row, column and total percentages; for a 2 by 2 table also Fisher's exact test, the odds ratio and the relative risk with confidence intervals. Each cell shows whatever you tick under Cells and Percentages, separated by vertical bars. If you untick all of them the table falls back to the observed counts and says so, rather than printing empty cells.

Two proportions: z test

Compares a success proportion between two groups using the pooled z test taught in the course, with the confidence interval for the difference and the success-failure check in each group. Summary mode takes successes and n per group.

distrACTION

Calculators, no data needed (Custom can also read two columns). Each one draws the distribution and shades the probability.

• Binomial: P(X = x), P(X at most x), P(X at least x), P(x1 to x2), plus mean and SD of the distribution.
• Poisson, Geometric, Hypergeometric, Discrete Uniform: the same layout as Binomial, with P(X = x), less than, at most, more than, at least and between, the mean, variance and SD, a full table and a shaded bar graph. Geometric lets you choose whether X counts the trials up to the first success (1, 2, 3, ...) or the failures before it (0, 1, 2, ...), since textbooks use both.
• Custom (discrete x and P(x)): any discrete probability distribution. Either put x in one column and P(x) in another (columns named x and p(x) are picked automatically), or type both lists separated by commas. Gives the mean (expected value), variance and SD, a work table with x P(x) and (x minus mean)^2 P(x) to match the hand method, and the probability P(X = x), less than, at most, more than, at least, or between. The bar graph shades that probability and marks the mean. It stops with a message if the probabilities are not between 0 and 1 or do not add to 1.
• Normal: probabilities below, above or between values, or the value for a given percentile (quantile). Type z scores by using mean 0 and SD 1.
• Uniform (continuous) and Exponential: probabilities below, above or between values, and percentiles, with mean and SD and the shaded area. Exponential takes the mean; if you are given a rate lambda, the mean is 1/lambda.
• T-Distribution: tail probabilities for a t statistic, or the critical t for a confidence level and df.
• Chi-square and F: right-tail p-value for a statistic, or the critical value for alpha.
• Sample size for a margin of error: for a proportion (with a guess for p, 0.5 if unknown) or a mean (with a guess for s).
• Power and sample size for a test: opens the Advanced power calculator.

Nonparametric

Rank-based alternatives for skewed data, ordinal scores or small samples.

• Mann-Whitney U: two independent groups (alternative to the independent t-test). Reports U, the p-value and the group medians.
• Wilcoxon signed-rank and sign test: paired data, or one column against a constant. Both tests are shown; the sign test uses only the direction of each difference.
• Kruskal-Wallis: three or more groups (alternative to one-way ANOVA). Follow a significant result with Mann-Whitney tests on the pairs that matter.

Learn

Teaching tools that show where the theory comes from. The first four run with nothing loaded, so they work on a projector in a room where no one has a file open; the rest can use a column of your data as the population.

• Coin flips and the law of large numbers: one coin, two coins, three coins, a die, two dice, or any probability you choose. It shows the first fifty outcomes, a table of the proportion after 10, 25, 50, 100, 250 and more trials, and the running proportion settling toward p. A second plot shows the count drifting further from the expected count even while the proportion settles, which is the answer to "19 percent, so 2 of the next 10 will be priority 1" and to "tails is due."
• The Central Limit Theorem: pick a population shape (right-skewed, normal, uniform, two humps, yes or no, or a column of your data) and several sample sizes at once. The sampling distributions are drawn on the same axes, with a table comparing the simulated SD against sigma / sqrt(n) and the skewness falling as n grows. The point students miss: the center never moves, the spread shrinks by sqrt(n), and only the shape is the theorem.
• What a confidence interval means: draws many samples from a population only you can see, builds an interval from each, and plots them with the true value as a fixed vertical line. It reports the actual capture rate against the stated level. The truth never moves; the interval does. Uses t with n - 1 degrees of freedom for a mean, and warns when n p or n (1 - p) falls under 10 for a proportion.
• What a p-value is: forces H0 to be true, draws thousands of fresh samples from that world, and counts how many land at least as far out as your result. For a proportion it prints the simulated p, the exact binomial p and the z formula p side by side, and explains the gap when the normal approximation is off.
• Mean versus median: click the number line to add values, click a dot to remove one. A long tail or one far value drags the mean and barely moves the median.
• Regression: outliers and influence: click to add points to a scatterplot and watch the least-squares line, r and the slope refit against the starting line. A point far out in x and off the pattern is influential; an outlier in the middle of the x range is not.
• Guess the correlation: a fresh scatterplot each time; type your guess for r, check it, and keep a running average of how far off you are.
• Sampling distribution simulator: pick a population (a column of your data, or a yes or no population with a chosen p), a statistic, a sample size and a number of samples. It draws the population, then the distribution of the sample statistic, and compares its SD with the formula (sigma / sqrt(n) or sqrt(p(1 minus p)/n)). Watch it turn normal as n grows.
• Bootstrap: a confidence interval with no formula. Resample with replacement, recompute the statistic thousands of times, take the middle 95 percent.
• Permutation: what a p-value is. Shuffle the group labels, recompute the difference, count how often the shuffled world beats the real one.
• Bayesian: prior, data, posterior on one picture, for a proportion or a mean.

Advanced

For second courses, research methods and upper-division work. Each card still ends with a plain-English reading.

Results

• Decimal places shown: 2, 3 (default), 4 or 6.
• Print or save as PDF: prints only the results (data table and menus are hidden). Use your browser's Save as PDF.
• Export results as HTML: one file with every card and graph, opens in any browser, good for submitting.
• Save session: a .sww.json file with the data, filter, analyses and graphs. Open a saved session brings it all back, on any computer.
• Clear analyses and Clear graphs empty the tabs. Individual cards have their own remove button.

8. Honest graphs

Statistics software will happily draw a misleading picture. Stats Without Walls refuses to, and prints the rules it followed under each graph so students learn them.

• The count axis starts at zero and cannot be zoomed on bar plots and histograms. A difference of two students can never be stretched to look like a cliff.
• Equal bins with nice edges. Histogram bins are all the same width, chosen from 1, 2, 2.5, 5, 10 times a power of ten, with tick marks on the edges. Each class includes its left edge; the last class also includes the maximum, so every value is counted once. When you group by a variable, all groups share the same bins so their shapes can be compared.
• Ordered scales stay in order. Freshman to Senior, Never to Always, Strongly disagree to Strongly agree, 1 to 5 and similar scales are drawn in their natural order. Sorting them tallest first hides the shape of the distribution.
• Same width for every bar; only height carries information. No 3D, no pictograms.
• Pies are flat and carry their percent and count, so nobody judges angles by eye.
• Numbers are on the picture. The count or percent is printed above each bar and bin by default, and a frequency table can sit under the graph.

9. Writing up a result

Use the "copy text" button, then write one paragraph with these parts: what was compared, the statistic with its degrees of freedom, the p-value, the decision at alpha, and what it means in context. The blue box gives you most of this sentence.

Always report the condition check. If it failed, say so and report the nonparametric or exact result instead.

10. Saving and sharing work

• Your browser remembers the current dataset, filter and results between visits on the same computer. This is a convenience, not a backup.
• For homework, Results, Export results as HTML or Print or save as PDF and upload the file.
• To continue later or on another computer, Results, Save session and keep the .sww.json file.
• To share a dataset with a classmate, Data, Download data as CSV.

11. How it differs from other tools

Menus, option names and table names match the course instructions, so they work as written. Where common statistics software and the course textbook disagree, Stats Without Walls follows the textbook:

12. Troubleshooting

13. Sample datasets

A codebook for the real-world datasets is in the app's data folder (README_real_world_datasets).

14. How the numbers were checked

Every procedure was run on the same data in R (and the jmv package where it exists) and compared to four decimals: t-tests, proportion tests, ANOVA and Tukey, chi-square, regression coefficients and intervals, nonparametric tests, multiple and logistic regression with diagnostics, two-way and repeated measures ANOVA, count, multinomial and ordinal models, power calculations, time-series decomposition and autocorrelation, PCA, factor analysis and varimax rotation, Cronbach's alpha, k-means, Kaplan-Meier, log-rank, Cox regression and the Bayesian conjugate updates. Distributions come from jStat; graphs are drawn with Plotly.

The distribution calculators (binomial, custom discrete, Poisson, geometric, hypergeometric, discrete uniform, uniform, exponential) were checked against R's d, p and q functions; the z tests, the five proportion intervals (against prop.test and binom.test), the variance tests (against var.test), Levene's test (against car::leveneTest), kurtosis, percentiles and the regression confidence and prediction intervals (against predict) the same way. Simulated data were checked by their mean and SD against the distribution they came from.

The Learn demonstrations are checked the same way. The simulated sampling distributions are compared against sigma / sqrt(n); the confidence interval demo is checked by its capture rate against the stated level, which also falls below that level on purpose when the success-failure condition fails; and the p-value demo is checked against the exact binomial, computed independently. Where the z formula and the exact answer disagree, the app prints both and says why rather than hiding the gap.

Stats Without Walls was built by Safaa Dabagh for students at Santa Monica College, West Los Angeles College and Loyola Marymount University, and is free for anyone to use.