simple random sample

(noun)

A sample in which each individual is chosen randomly and entirely by chance, such that each individual has the same probability of being chosen at any stage during the sampling process, and each subset of $k$ individuals has the same probability of being chosen for the sample as any other subset of $k$ individuals.

Related Terms

Examples of simple random sample in the following topics:

  • Three sampling methods (special topic)

    • Here we consider three random sampling techniques: simple, strati ed, and cluster sampling.
    • Simple random sampling is probably the most intuitive form of random sampling.
    • Cluster sampling is much like a two-stage simple random sample.
    • Then we sample a fixed number of clusters and collect a simple random sample within each cluster.
    • Examples of simple random, stratified, and cluster sampling.

  • Random Samples

    • A simple random sample is a subset of individuals chosen from a larger set (a population).
    • A simple random sample is an unbiased surveying technique.
    • Simple random sampling is a basic type of sampling, since it can be a component of other more complex sampling methods.
    • Although simple random sampling can be conducted with replacement instead, this is less common and would normally be described more fully as simple random sampling with replacement.
    • Conceptually, simple random sampling is the simplest of the probability sampling techniques.

  • Assumption

    • Your data should be a simple random sample that comes from a population that is approximately normally distributed.
    • You use the sample standard deviation to approximate the population standard deviation.
    • When you perform a hypothesis test of a single population mean µ using a normal distribution (often called a z-test), you take a simple random sample from the population.
    • The population you are testing is normally distributed or your sample size is sufficiently large.
    • When you perform a hypothesis test of a single population proportion p, you take a simple random sample from the population.

  • Random Sampling

    • A simple random sample (SRS) is one of the most typical ways.
    • Also commonly referred to as a probability sample, a simple random sample of size n consists of n individuals from the population chosen in such a way that every set of n individuals has an equal chance of being in the selected sample.
    • Simple random samples are not perfect and should not always be used.
    • At this stage, a simple random sample would be chosen from each stratum and combined to form the full sample.
    • Categorize a random sample as a simple random sample, a stratified random sample, a cluster sample, or a systematic sample

  • Lab 2: Sampling Experiment

    • The student will demonstrate the simple random, systematic, stratified, and cluster sampling techniques.
    • In this lab, you will be asked to pick several random samples.
    • In each case, describe your procedure briefly, including how you might have used the random number generator, and then list the restaurants in the sample you obtained
    • Pick a stratified sample, by city, of 20 restaurants.
    • Pick a cluster sample of restaurants from two cities.

  • Inferential Statistics

    • The most straightforward is simple random sampling.
    • In this sense, we can say that simple random sampling chooses a sample by pure chance.
    • Was the sample picked by simple random sampling?
    • Just this defect alone means the sample was not formed through simple random sampling.
    • Sometimes it is not feasible to build a sample using simple random sampling.

  • Summary

    • Each member of the population has an equal chance of being selected- Sampling Methods

  • Sampling

    • The easiest method to describe is called a simple random sample.
    • Besides simple random sampling, there are other forms of sampling that involve a chance process for getting the sample.
    • Number each department and then choose four different numbers using simple random sampling.
    • However for practical reasons, in most populations, simple random sampling is done without replacement.
    • Determine the type of sampling used (simple random, stratified, systematic, cluster, or convenience).

  • Sampling Techniques

    • In a simple random sample (SRS) of a given size, all such subsets of the frame are given an equal probability.
    • However, SRS can be vulnerable to sampling error because the randomness of the selection may result in a sample that doesn't reflect the makeup of the population.
    • As long as the starting point is randomized, systematic sampling is a type of probability sampling.
    • Cluster sampling generally increases the variability of sample estimates above that of simple random sampling, depending on how the clusters differ between themselves, as compared with the within-cluster variation.
    • In quota sampling the selection of the sample is non-random.

  • Samples

    • The best way to avoid a biased or unrepresentative sample is to select a random sample, also known as a probability sample.
    • A random sample is defined as a sample wherein each individual member of the population has a known, non-zero chance of being selected as part of the sample.
    • Several types of random samples are simple random samples, systematic samples, stratified random samples, and cluster random samples.
    • A sample that is not random is called a non-random sample, or a non-probability sampling.
    • Some examples of nonrandom samples are convenience samples, judgment samples, and quota samples.