Sampling in statistics means choosing a smaller group, the sample, from a larger population in order to estimate characteristics of the whole. Methods include simple random sampling, stratified sampling (random draws within subgroups), cluster sampling and systematic sampling, versus non-random convenience samples.
Why it matters
Measuring everyone is often impossible or too costly, but conclusions are only as good as the sample. Random selection lets statistical theory quantify uncertainty through margins of error and confidence intervals. Bias creeps in when the sample systematically differs from the population, for instance surveying only users who answer emails. Larger samples reduce random error but do not fix bias.
Example
Population: 100,000 customers (80% desktop, 20% mobile)
Stratified sample of 1,000: 800 desktop + 200 mobile
-> mobile users are guaranteed to be representedAnalytics tools sometimes sample traffic for speed, so reported figures can be estimates.
Common mistakes
- Using a convenience sample and generalising to everyone.
- Ignoring non-response bias when only some people reply.
- Sampling data for monitoring or logging in a way that misses rare errors.
Related
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यह पेज सामान्य जानकारी है, जिसे हमारी संपादकीय टीम ने लिखा है और दस्तावेज़ीकरण बदलने पर अपडेट किया जाता है। सर्वर और प्रदाता अलग-अलग होते हैं, इसलिए बदलाव पहले सुरक्षित तरीके से जाँचें। देखें: संपादकीय नीति और अस्वीकरण.