Planning Difference in Differences Power
Difference in differences compares change, not a single final value. A treated group has a before and after mean. A control group has the same two means. The calculator subtracts the control change from the treated change. That value is the estimated effect. In applied physics projects, this can describe a power saving, sensor shift, heat loss change, or lab intervention effect.
Why Correlation Matters
Repeated readings on the same unit are usually related. A high correlation lowers the noise in each change score. A low correlation raises uncertainty. That is why the form asks for a pre post correlation. It also asks for the common standard deviation. These two values shape the standard error more than many users expect.
Sample Size Meaning
Power is the chance of finding the selected effect when it is truly present. It depends on the effect size, variation, alpha level, tail choice, and group sizes. Larger samples reduce the standard error. Stronger effects are easier to detect. Strict alpha settings need more evidence, so they often require more units.
Using Results Carefully
The output gives the difference in differences estimate, standard error, confidence range, z score, achieved power, required treated units, required control units, and minimum detectable effect. These values are planning aids. They do not prove that assumptions are correct. Check whether the equal variance assumption is reasonable. Also confirm that the treated and control groups follow parallel trends before the intervention.
Practical Study Tips
Use pilot data whenever possible. If pilot data is not available, test several standard deviation values. Small assumption changes can move power sharply. Keep records of every input used in a report. Export the calculation when sharing a plan with reviewers. The method is simple, but the design still needs domain knowledge. A clear design also defines the unit of analysis. The unit may be a device, panel, room, meter, patient, classroom, or site. Avoid mixing units without a reason. If clusters are used, inflate the sample size for clustering. If dropout is expected, add reserves before the study begins. Good planning protects time, budget, and interpretation. It makes later peer review and quality checks easier for everyone involved in measurement and analysis.