Calculator
Example Data Table
| Scenario | Treatment pre | Treatment post | Control pre | Control post | Effect | Sample per group | Use case |
|---|---|---|---|---|---|---|---|
| Cooling plate coating | 25 | 22 | 25 | 24 | -2 | 80 | Thermal shift test |
| Sensor calibration | 1.20 | 1.05 | 1.18 | 1.14 | -0.11 | 120 | Drift reduction |
| Shielding treatment | 44 | 31 | 43 | 38 | -8 | 40 | Radiation attenuation |
Formula Used
Difference in differences:
DiD = (Treatment post mean − Treatment pre mean) − (Control post mean − Control pre mean)
Change score variance:
Variance change = SD pre² + SD post² − 2 × r × SD pre × SD post
Standard error:
SE = √[((Var change treatment / effective n treatment) + (Var change control / effective n control)) × design effect × (1 − R²)]
Design effect:
Design effect = 1 + (average cluster size − 1) × intraclass correlation
Normal power approximation:
Power uses the noncentral signal ratio |effect| / SE and the selected alpha level. Two sided tests use alpha / 2.
How to Use This Calculator
- Enter the four pre and post means for treatment and control groups.
- Add standard deviations for each measurement period.
- Enter treatment and control sample sizes.
- Set the expected pre post correlation.
- Choose alpha, test type, and desired power.
- Add attrition, cluster size, intraclass correlation, and covariate R squared if needed.
- Press the calculate button.
- Review power, minimum detectable effect, and suggested sample size.
Article
Why Difference in Differences Power Matters
Difference in differences compares two changes. One change comes from a treated system. The other comes from a control system. In physics work, this idea can test an intervention, calibration, shielding change, cooling method, or material treatment. The method removes shared drift. It also reduces bias from stable baseline gaps.
Power planning asks a simple question. Will the experiment detect the expected shift? A high powered design is more likely to find a real effect. A low powered design can miss useful behavior. That risk matters when instruments are costly, runs are slow, or samples are limited.
Key Inputs
The calculator uses four means. They are treatment before, treatment after, control before, and control after. Their contrast gives the expected effect. Standard deviations describe measurement spread. Sample sizes describe independent units. Pre post correlation controls how noisy each change score becomes.
Advanced settings improve realism. Attrition reduces usable samples. Clustering inflates uncertainty when units share a batch, lab, chamber, coil, or sensor plate. An intraclass correlation describes that shared similarity. Covariate adjustment can reduce residual variance when a strong predictor is measured.
How Results Help
The result gives the difference in differences estimate. It also reports the standard error, test statistic, approximate p value, power, minimum detectable effect, and suggested sample size. These values support planning before data collection. They also help review a completed pilot.
A larger effect increases power. More samples increase power. Lower standard deviation increases power. Higher pre post correlation can help because change scores become cleaner. Larger cluster size can hurt when cluster members behave alike.
Use the sample size section early. It shows whether the current plan is enough. It also shows how many units may be enrolled after expected losses during trials.
Good Practice
Use realistic pilot data when possible. Avoid choosing a tiny standard deviation only to make power look strong. Match the unit of analysis to the design. A sensor reading, beam, wafer, batch, or chamber can represent different levels.
Power is not proof. It is a design guide. Results still depend on assumptions. Check sensitivity by changing effect size, variance, correlation, and intraclass correlation. A robust design should remain useful under several reasonable settings.
FAQs
What is difference in differences power?
It estimates the chance of detecting a true treatment effect when comparing before and after changes between treatment and control groups.
Can this be used for physics experiments?
Yes. It can support tests involving calibration changes, thermal systems, shielding, sensors, materials, or repeated experimental measurements.
What does the target effect override do?
It lets you plan power for an expected effect instead of using the effect calculated from the four entered means.
Why is pre post correlation important?
It affects change score variance. Higher correlation often reduces noise because each unit is compared more closely with itself.
What does attrition mean?
Attrition is the expected percent of samples lost before final analysis. The calculator reduces effective sample size by this amount.
What is the intraclass correlation?
It measures similarity inside clusters. Higher values increase the design effect and usually reduce statistical power.
What is minimum detectable effect?
It is the smallest effect the current design can detect at the chosen alpha and desired power under normal assumptions.
Should I trust the result exactly?
No. Treat it as a planning estimate. Check several scenarios with different variance, correlation, attrition, and cluster assumptions.