Stages in Sampling: || Types of Sampling:

Outline:

Introduction

You've studied probability distributions like the binomial and normal distributions extensively. The normal distribution's shape is governed by its mean and standard deviation, whereas the binomial distribution's shape is determined by p. These numerical descriptive metrics, known as parameters, are required to calculate the likelihood of seeing sample results.

  • Consider the following instance. A massive tank of fish is being brought to the lake from a hatchery. The average length of the fish in the aquarium is what we're looking for. We randomly choose twenty fish and use the sample mean to estimate the population mean instead of measuring all of the fish.
  • A pollster is sure that the responses to his “agree/disagree” questions will follow a binomial distribution, but p, the proportion of those who “agree” in the population, is unknown.
  • An agronomist believes that the yield per acre of a variety of wheat is approximately normally distributed, but the mean μ standard deviation σ of the yields are unknow.

    • In these cases, you must rely on the sample to learn about these parameters. The proportion of those who “agree” in the pollster’s sample provides information about the actual value of p. The mean and standard deviation of the agronomist’s sample approximate the actual values of μ and σ.

    If a sample is to be used, it is critical that the persons picked are representative of the total population, regardless of the method used. This could imply focusing on hard-to-reach demographics. Sampling can be used to make population-wide generalizations or inferences about a hypothesis. In essence, the sampling procedure is what determines it.

    Sampling

    In order to make statistical inferences and estimate population characteristics, sampling is a way of selecting individuals or a subset of the population. Several sampling procedures are widely used in research so that researchers do not have to survey the entire community in order to obtain useful information.

    Sampling Frame

    The actual collection of units from which a sample has been drawn is referred to as the sampling frame (synonyms: "sample frame," "survey frame"). The sampling frame should, in the ideal case, correspond to the population of interest.

    Types of Sampling Techniques:

    • Probability Sampling - Probability sampling is a method of sampling in which a researcher selects individuals from a population at random based on a set of criteria. All members have an equal chance of being included in the sample using this selection criteria.

    Here are the types of Probability Sampling.

    *Simple Random Sampling - One of the most efficient ways to save time and resources is to employ probability sampling. It's a reliable data collection method in which every single member of a population is chosen at random and simply by chance. A person's chance of being picked to participate in a study is the same for everyone.

    *Stratified Random Sampling - This technique divides the population into smaller groups that do not overlap but nonetheless accurately represent the entire population. Before sampling, these groupings can be categorized, and then a sample chosen from each category separately.

    *Systematic Random Sampling - Researchers utilize the sampling strategy to pick sample individuals of a community at regular intervals. It demands selecting a sample starting point and sample size that can be repeated on a regular basis. This type of sampling method requires the least amount of time because it has a predetermined range.

    We divide our population of size N into k subgroups for a sample of size n. From the first subgroup, the first of our k elements is chosen at random.

    * Cluster Random Sampling - When a research subject is too large, the researcher should divide it into smaller pieces of the same or equivalent size and pick at random from the smaller units. The entire population is expected to be partitioned into a smaller number of units, still made up of clusters of smaller units, with some of these cluster units being randomly selected for inclusion in the general sampling.

    *Multi-stage Random Sampling - Typically, a multi-stage sample approach is used in large geographical area inquiries for the entire country. Multistage sampling entails combining several probability sampling methods in the most effective and efficient way possible. The population is classified into clusters, which are then segmented and grouped into a variety of subgroups (strata) based on their similarities.

    • Non-Probability Sampling - The non-probability approach is a sampling technique in which a researcher or statistician receives feedback based on their sample selection abilities rather than a pre-determined selection process. A survey conducted with a non-probable sample is likely to be skewed and may not accurately reflect the target demographic.

    Here are the list of non-probability sampling:

    *Convenience sampling - Because of the simplicity with which the researcher can conduct it and contact the subjects, it is commonly referred to as convenience sampling. Researchers have almost no power over the sample items they choose, and they do so only on the basis of proximity rather than representativeness.

    *Judgmental or purposive sampling - The researcher's evaluation of who will provide the most helpful information for the study's goals informs the sample design.

    *Quota Sampling - This sampling technique involves the selection of members based on a pre-determined standard.

    *Chain Referral or Snowball Sampling - When it's tough to track down subjects, researchers use this sampling strategy. Is a selected design procedure that is typically carried out through the use of networks.

    Thank you for Reading!

    I'd love to hear your thoughts about the Introduction of Sampling Techniques. Feel free to leave your comment section below.

    References:

  • https://www.statistics.com/glossary/sampling-frame/
  • https://www.investopedia.com/terms/s/sampling-distribution.asp
  • https://www.khanacademy.org/math/statistics-probability/sampling-distributions-library
  • https://www.geneseo.edu/~pyan/bs/c07.pdf
  • https://online.stat.psu.edu/stat500/lesson/4

    • Is this article useful to you?

    Stages in Sampling: | | Types of Sampling: