Free statistics practice datasets

Three research starter kits to turn a statistics lesson into hands-on practice. Download the CSV, follow the steps, and explore the matching tutorial.

Made for teaching and self-study

Every record is synthetic. Download and reuse the datasets freely, without an account. They illustrate workflows and are not evidence about real populations.

To analyze a downloaded CSV in DataStatPro, import it using an eligible Educational, Standard, Pro, or Edu Pro account. Guest Access uses the app’s built-in sample datasets. Students, teachers, and researchers with a valid email domain containing .edu can use a free Educational Account; everyone can try Standard free for 15 days. See account features.

Download reuse notes · Choose a statistical test

Start with descriptive statistics

Describe a student survey

How do study time and satisfaction vary in a small practice survey?

24 synthetic participants · CSV · No missing values

Download practice CSV

Variables

ColumnMeaning
participant_idSynthetic identifier; exclude from calculations
study_hoursHours per week
satisfactionOrdered response from 1 (low) to 5 (high)

Try this workflow

  1. Import the CSV and check that each row is one participant.
  2. Summarize study_hours with a histogram, mean, median, and standard deviation.
  3. Report satisfaction as counts and percentages; it is an ordinal response.
  4. Describe the distribution and any unusual observations without generalizing beyond these synthetic records.

Follow the tutorial · Explore the analysis tool

Use this in a class

Inspect relationships

Explore a correlation

What relationship appears between practice hours and assessment scores in simulated data?

24 synthetic participants · CSV · No missing values

Download practice CSV

Variables

ColumnMeaning
participant_idSynthetic identifier; exclude from calculations
practice_hoursHours of practice
assessment_scorePractice assessment points out of 100

Try this workflow

  1. Import the CSV and create a scatterplot of practice_hours and assessment_score.
  2. Inspect form and outliers before selecting Pearson or Spearman correlation.
  3. Report the selected coefficient with its sample size and uncertainty where available.
  4. Explain why a relationship in these intentionally constructed data does not establish causation.

Follow the tutorial · Explore the analysis tool

Use this in a class

Understand repeated observations

Compare paired measurements

How do practice scores change when the same fictional participants complete two assessments?

20 synthetic participants · CSV · No missing values

Download practice CSV

Variables

ColumnMeaning
participant_idSynthetic identifier; matches each pair
before_scoreAssessment points before practice
after_scoreAssessment points after practice

Try this workflow

  1. Keep before_score and after_score on the same participant row.
  2. Inspect the distribution of within-participant differences, not only the two original columns.
  3. Use the paired-test tutorial to assess whether a paired t-test is appropriate.
  4. Report the direction and size of change and acknowledge that these data were constructed for teaching.

Follow the tutorial · Explore the analysis tool

Use this in a class

Bring a kit into your classroom

Choose a kit, give learners its question and CSV, and ask them to explain their analysis choices before reporting a result. Use the teaching link card below in a course page or learning platform. Students can download the data without signing in.

For assessed work, use your own research question and data. These practice exercises do not replace a study protocol or justify a sample-size choice.

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This cites the web page. When reporting an analysis, also record the software version, method, options, and access date you used.

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