Enter Study Inputs
Provide means, standard deviations, and sample sizes for treatment and control groups across pre and post periods.
Example Data Table
This sample matches the default form values and demonstrates how the four-cell layout supports a standard difference in differences design.
| Group | Period | Mean Outcome | SD | N |
|---|---|---|---|---|
| Treatment Group | Pre | 120 | 14 | 40 |
| Treatment Group | Post | 150 | 16 | 40 |
| Control Group | Pre | 115 | 13 | 38 |
| Control Group | Post | 123 | 15 | 38 |
Formula Used
The calculator estimates the net treatment effect by comparing outcome changes across treatment and control groups between pre and post periods.
Difference in Differences Effect:
DiD = (Treatment Post − Treatment Pre) − (Control Post − Control Pre)
Counterfactual Treated Post:
Counterfactual = Treatment Pre + (Control Post − Control Pre)
Approximate Standard Error:
SE = √[(SDT,Pre2/NT,Pre) + (SDT,Post2/NT,Post) + (SDC,Pre2/NC,Pre) + (SDC,Post2/NC,Post)]
Test Statistic:
Z = DiD ÷ SE
Confidence Interval:
CI = DiD ± Zcritical × SE
This inference is an approximate independent-cells approach. For panel data, clustered designs, or repeated observations, use a model with proper variance estimation.
How to Use This Calculator
- Enter the outcome name and optional unit label.
- Name your treatment and control groups clearly.
- Input pre and post means for both groups.
- Enter standard deviations and sample sizes for each cell.
- Choose a confidence level and output precision.
- Click the calculate button to show the results above the form.
- Review the DiD effect, counterfactual post value, p value, and interval.
- Use the CSV or PDF buttons to export the result summary.
Interpretation Notes
A positive DiD estimate means the treatment group improved more, or declined less, than the control group after the intervention.
A negative DiD estimate means the treatment group underperformed the control group after adjusting for common time movement.
The method relies heavily on the parallel trends assumption. Without it, the estimated treatment effect may be biased.
When outcomes are percentages, counts, or repeated measures, a regression-based specification may be more appropriate than a simple summary-cell calculation.
FAQs
1) What does difference in differences measure?
It measures the net impact of an intervention by comparing before-after change in a treatment group against before-after change in a control group.
2) Why do I need both treatment and control groups?
The control group captures background trends that may affect outcomes even without treatment. Subtracting that trend helps isolate the intervention effect.
3) What is the counterfactual treated post value?
It is the estimated post-treatment outcome for the treatment group if it had followed the control group’s time trend instead of receiving treatment.
4) What assumption matters most in this method?
The key assumption is parallel trends. Without treatment, both groups should have moved similarly over time on average.
5) Can I use this for percentages or monetary outcomes?
Yes. The calculator works with any continuous outcome scale as long as the means, standard deviations, and sample sizes are entered consistently.
6) Is the p value exact?
No. This page uses an approximate normal-based test from four summary cells. Complex designs may require clustered, paired, or regression-based standard errors.
7) What if my sample sizes differ across periods?
Different sample sizes are allowed. Enter the correct N for each cell, because the standard error calculation uses all four sample counts separately.
8) When should I avoid this simple calculator?
Avoid it for highly skewed data, serial correlation, repeated panels, or clustered units when a more formal regression model is needed.