Each Six Sigma project will require the team to take a sample from the population they are studying. Lean Six Sigma courses offer a variety sampling methods. Six Sigma Measure is the phase that applies statistics sampling. Six Sigma Green Belt training will teach you how to distinguish between two types of sampling methods: non-probability and probability. Let’s take a closer look at these sampling methods and sub-types.
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Probability sampling methods
Probability sampling methods use known probabilities to select units from the population. A simple random sample, proportional to sample size, etc. Probability sampling methods allow you to determine which sampling units belong and how likely each sample will be selected. This means that the probability of a sample being selected in this way is known.
These sampling methods are examples for probability sampling methods.
Simple Random Sampling
Probability Sampling Methods: Simple Random Selection (SRS).
Simple random sampling is a special type of random sample when we look at probability sampling methods. If each unit of the population has a chance of being selected, a sample is simple random sampling. This is the most basic sampling technique. It allows us to select a group of subjects from a larger population or sample for study. Each person is selected entirely by chance. Every member of the sample has an equal chance of being included. Every possible sample of any size has the same chance to be selected. It is also known by the term “unrestricted random sampling”.
There are two types of simple random sampling methods. The first is SRS without replacement, and the second is SRS with substitution. It is called sampling with replacement if a population element can only be selected once. A population element that can only be selected once is called sampling without replacement.
Probability Sampling Methods – Stratified Random Sampling
Stratified sampling is the most common sampling method used in surveys. This technique is used to reduce population heterogeneity and diversity, and increase the efficiency of estimates. Stratification is the division of a population into groups. This method divides the population into subgroups or strata. Each stratum should be created in such a manner that it is homogeneous and as similar as possible. A random sample of each stratum may then be taken and combined to create the required sample. A’stratum is an individual group.
Stratified sampling should be used to:
Divide the population into groups, also known as “strata”
Take a simple random sample of each group. Also known as “stratum”
Collect data about each sampling unit that was randomly selected from each group, such as stratum.
There are two types of stratified sampling methods available: proportional or non-proportional. The proportional sampling method gives equal and proportionate representation to subgroups and strata. If the number of items in the sample is large, the sample will be larger and vice versa. The sample size is denoted with ‘n’. Each stratum is assigned a sample size so that the sample fraction remains constant. This is done by n/N = C. Each stratum is then represented according to its size.
In the non-proportionate sampling, all sub-strata are represented equally