Descriptive Statistics: How to Run It Online and Report It in APA7
Descriptive statistics summarise a variable with a few numbers: where its centre is, how spread out it is, and what shape it has.
What is Descriptive Statistics?
Descriptive statistics summarise a variable with a few numbers: where its centre is, how spread out it is, and what shape it has.
When to use it
Use them at the start of every project, for every variable, before any test: to check data entry, spot outliers, describe your sample, and decide which tests are appropriate.
Example question: How old were my participants, and how were stress scores distributed?
Preparing your data
One row per participant (case) and one column per variable. Put a short, unique name in the first row of each column. Keep numbers numeric (no units or text inside the cells) and leave missing values empty.
Example dataset (simulated; first rows of 60): id (participant), group (Control or Intervention), age (years), stress (scale score).
| id | group | age | stress |
|---|---|---|---|
| 1 | Intervention | 29 | 26.1 |
| 2 | Intervention | 23 | 11.1 |
| 3 | Control | 28 | 27.3 |
| 4 | Intervention | 31 | 33.1 |
| 5 | Intervention | 27 | 15.6 |
| 6 | Intervention | 25 | 17.9 |
Download the full example CSV to practise. If the tool asks for a different layout, follow the instructions on its page.
How to run it on StatFas
- Open the Descriptive Statistics tool. You do not need an account to run an analysis.
- Enter your data or upload your file, laid out as described in Preparing your data.
- Select your variables. Select every numeric variable you want to summarise. If the tool lets you split by a grouping variable, add it to get one set of numbers per group.
- Choose any options the tool offers (for example the confidence level, a post hoc test or the error bar type) and run the analysis.
- Read the results on the page. Check the assumptions first, then the test statistic, the p-value and the effect size.
- To save your results, log in or create a free account. Every analysis stays free; the account is only needed to download.
Assumptions and checks
- No test assumptions are needed to describe a sample, but the right summary depends on the measurement level: mean and SD for interval/ratio data, median and interquartile range for skewed or ordinal data.
- Check the minimum and maximum of every variable for impossible values (for example an age of 250 or a 1–7 scale containing an 11).
- Look at skewness and kurtosis, or a histogram, before deciding whether the mean is a fair summary.
- Report the number of valid cases for each variable, because missing data make n differ between variables.
Key numbers to look at
- n (valid cases) and the amount of missing data.
- Mean (M) and standard deviation (SD): the typical value and the typical distance from it.
- Median and minimum/maximum (or interquartile range) for skewed variables.
- Skewness and kurtosis: values near 0 suggest a roughly symmetric, normal-like shape; large values warn of a long tail.
Effect size
Descriptive statistics have no effect size. They are the numbers that later effect sizes are built from.
Benchmarks such as small, medium and large are conventions (Cohen, 1988), not rules. Interpret size in the context of your field.
Reporting Descriptive Statistics in APA7 style
Template
Skewed variable: Median = XX.X, IQR = XX.X–XX.X.
Several variables: present them in one APA table with columns n, M, SD, minimum and maximum.
Worked example (simulated data)
The numbers below were computed from the example CSV above, so you can reproduce them. They illustrate the format and are not real findings.
| Variable | n | M | SD | Min | Max | Skewness | Kurtosis |
|---|---|---|---|---|---|---|---|
| age | 60 | 24.92 | 3.73 | 18 | 34 | 0.00 | −0.48 |
| stress | 60 | 22.59 | 6.02 | 9.7 | 35.2 | −0.05 | −0.26 |
APA7 reminders: italicise statistical symbols (M, SD, t, F, p, r), report exact p-values to two or three decimals (use p < .001 for very small values), omit the leading zero for values that cannot exceed 1 (.45, not 0.45), and put degrees of freedom in parentheses. See the APA7 cheat sheet.
Common mistakes
- Reporting only the mean. Always give a measure of spread (SD) with it.
- Reporting a mean and SD for a nominal variable such as gender. Use counts and percentages instead (see Frequency Analysis).
- Forgetting to say how many cases were excluded for missing data.
Related analyses and alternatives
- Frequency Analysis: For categorical variables, use counts and percentages.
- Bar Plot: To compare group means visually.
- Correlation: To describe how two numeric variables move together.
Not sure which analysis you need? Use the test chooser.
Frequently asked questions
What is the difference between standard deviation and standard error?
The standard deviation describes the spread of the scores in your sample. The standard error describes how precisely the sample mean estimates the population mean, and it shrinks as the sample grows. Describe your sample with the SD.
Should I report the mean or the median?
Report the mean and SD when a variable is roughly symmetric and measured on an interval scale. Report the median and interquartile range when it is clearly skewed or ordinal. Many papers report both.
References
American Psychological Association. (2020). Publication manual of the American Psychological Association (7th ed.). https://doi.org/10.1037/0000165-000
This guide is for learning and for preparing results for your own reports. You remain responsible for choosing an appropriate analysis, checking its assumptions and interpreting the results. Follow the formatting rules of your journal or institution where they differ from APA7.