Sampling Distribution

Population vs. Sample

  • A ==population== is the entire group that you want to draw conclusions about.
  • A ==sample== is the specific group that you will collect data from. The size of the sample is always less than the total size of the population.

Non-probability vs. Probability Sampling

  • ==Sampling== is the process of selecting a sample.
  • The ==non-probability samples== and ==probability samples== are two types of sample.
  • %%Non-probability%% samples are obtained conveniently, selected purposively, or are taken as volunteers.
    • They result from the use of judgement sampling, accidental sampling, purposive sampling, and the like
    • They should be not used for statistical inference.
  • A %%random sampling%% is a type of sampling in which the data is collected using randomization. It is also known as %%probability sampling%%.

Types of Probability Sampling

  • Simple random sampling
  • Systematic random sampling
  • Stratified random sampling
  • Cluster random sampling

Statistic Vs Parameter

  • When you collect data from a population or a sample, there are various measurements and numbers you can calculate from the data.
  • A parameter is a measure that describes the whole population.
  • A statistic is a measure that describes the sample.

Reasons for Sampling:

  1. Necessity

    1. Sometimes it’s simply not possible to study the whole population due to its size or inaccessibility

  2. Practicality

    1. It’s easier and more efficient to collect data from a sample.

  3. Cost-effectiveness

    1. There are fewer participant, laboratory, equipment, and researcher costs involved.

  4. Manageability

    1. Storing and running statistical analyses on smaller datasets is easier and reliable.