Sample size
The number of observations or other units included in a sample, study, or analysis.
Grokking Statistics
Sample size is the number of observations included in a sample.
- How it is used
- Uses sample size to judge how much evidence supports estimates of population means and confidence intervals.
- How deep it goes
- Explains notation, representativeness, sampling-distribution spread, confidence-interval precision, and small-sample t-distribution choices without deriving formulas.
- Examples
- Uses dorm sleep tracking, adult body temperatures, confidence intervals for sleep hours, and constrained counts of satellites or bridges.
- What you can do after
- Evaluate/understand how sample size affects uncertainty, interval width, and whether normal or t-based methods fit a mean estimate.
Effective Data Analysis
Sample size is the number of observations or participants included in a statistical sample.
- How it is used
- Uses sample size to plan inferential tests, assess detectable effects, and protect anonymity in analytical reporting.
- How deep it goes
- Explains power-based planning, alpha adjustments, test assumptions, large-sample limits, and a five-person privacy threshold.
- Examples
- Uses t-test power calculations, simulated normal samples, Kruskal-Wallis exercises, and an employee survey with an n=1 subgroup.
- What you can do after
- Apply sample-size planning with statsmodels and evaluate how sample size affects test results and privacy-safe reporting.
Statistics Every Programmer Needs
Sample size is the number of observations or trials included in a statistical sample or simulation.
- How it is used
- Determines the amount of evidence used to estimate variation, monitor process stability, or simulate outcome frequencies.
- How deep it goes
- Explains effects on precision, control limits, statistical power, and proportion variability, but does not derive sample-size selection rules.
- Examples
- Uses 500 simulated trials, scalable trial counts, control-chart samples, and p-chart proportions as concrete examples.
- What you can do after
- Apply it to choose trial counts, interpret control-chart sensitivity, and assess how sampling affects variability estimates.
Read how Statistics Every Programmer Needs explains sample size →
Related concepts
- power analysisproduced by · 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.
- statistical samplemeasures · A statistical sample is one or more observations selected or generated from a population or probability process for analysis.
- student's t-distributionconfigures · Student’s t-distribution is a family of continuous probability distributions with heavier tails than the normal distribution, used in inference when variability is estimated from data.
- varianceconfigured by · Variance is the expected squared deviation of a random variable from its mean, measuring the spread of its distribution.
- accuracycontrasts with · Accuracy is the degree to which an output, prediction, or measurement agrees with a correct or reference outcome under a specified criterion.
- np-chartused by · An np-chart is an attribute control chart that tracks the number of defective items in successive samples of fixed size.
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