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    Random Sampling

    Random sampling is a probability sampling method where every member of the target population has an equal, known chance of being selected.

    Random sampling (also called simple random sampling) is the gold standard for survey research because it eliminates selection bias and allows valid statistical inference to the broader population. Each unit is drawn independently with equal probability, typically via a random number generator applied to a complete sampling frame. The technique underlies confidence intervals and margin-of-error calculations — without randomization, those statistics are not meaningful. In practice, true random sampling is rare in commercial surveys; most teams use stratified or convenience sampling and disclose the trade-off.

    Formula

    P(selection) = 1 / N for every unit, where N = population size

    Example

    A team with 10,000 customers wants n = 400 responses. They generate 400 unique random integers between 1 and 10,000 and invite those customers. With response rate of 25%, they'd need to oversample to ~1,600 invites.

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    Related terms

    Stratified Sampling

    Stratified sampling divides the population into mutually exclusive subgroups (strata) and samples randomly within each, ensuring proportional or oversampled representation.

    Cluster Sampling

    Cluster sampling divides the population into naturally occurring groups (clusters) and randomly selects entire clusters, then surveys all or some members within them.

    Convenience Sampling

    Convenience sampling recruits whoever is easiest to reach — website visitors, email-list subscribers, social-media followers — and is the dominant approach in commercial surveys despite its limitations.

    Sample Size

    Sample size is the number of respondents needed for survey results to be statistically meaningful at a given confidence level and margin of error.

    Selection Bias

    Selection bias is a systematic error arising when the people included in a survey differ from the target population in ways that affect the outcome.

    Margin of Error

    Margin of error is the range within which the true population value likely falls, given the sample size and confidence level.