Power analysis
Power analysis is a statistical method for estimating a test’s ability to detect an effect under specified assumptions, often to determine an appropriate sample size.
Effective Data Analysis
An a priori power analysis determines the sample size required to achieve a desired probability of detecting an effect of a specified magnitude at a chosen significance level.
- How it is used
- Plans sample collection before t-tests or ANOVAs and supports defensible decisions about statistical evidence.
- How deep it goes
- Explains parameter choices, assumption checks, conservative targets, and implementation, but does not derive the underlying formulas.
- Examples
- Uses t-test simulations, Boston–New York weather data, and a four-group ANOVA calculation as concrete applications.
- What you can do after
- Apply it via statsmodels to estimate minimum sample sizes and plan appropriately powered statistical tests.
Experimentation for Engineers
Power analysis is the calculation of the sample size or sensitivity needed to limit the probability of failing to detect a specified effect.
- How it is used
- Designs A/B tests by choosing enough measurements to detect a practically significant change in a business metric.
- How deep it goes
- Derives the sample-size inequality and implements it numerically, but does not develop a general power-analysis theory.
- Examples
- Uses simulated trading systems, execution-cost variability, practical significance of 1.0 mip, and seven-versus-sixteen-measurement designs.
- What you can do after
- Build it from scratch by estimating variability, selecting a meaningful effect, and calculating a rounded-up measurement count.
Read how Experimentation for Engineers explains power analysis →
Hacking Cryptography
Power analysis is a side-channel technique that infers computational behavior or secrets from a device's power consumption.
- How it is used
- Infers private-exponent bits from power differences during cryptographic exponentiation.
- How deep it goes
- Traces an oscilloscope-based attack and contrasts operation leakage with constant-time implementation, stopping short of optimization details.
- Examples
- Uses RSA power traces, square-and-multiply for exponent 42, and the Montgomery powering ladder.
- What you can do after
- Apply it via an oscilloscope to analyze operation-dependent power traces and recognize constant-operation defenses.
Related concepts
- sample sizeproduces · The number of observations or other units included in a sample, study, or analysis.
- side-channel attackused by · A side-channel attack infers secret information from unintended physical or operational leakage produced by a system’s implementation rather than its intended outputs.
- effect sizeuses · An effect size is a quantitative measure of the magnitude of a difference, association, relationship, or other observed phenomenon, independent of statistical significance.
- statsmodelsuses · statsmodels is an open-source Python library for statistical modeling, estimation, inference, hypothesis testing, and statistical data analysis.
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