SAMPLING

• SAMPLE

  • Representation of the entire population
• POPULATION

  • Complete set of individuals having the same characteristics.
  • Consists of everything being studied in an inference procedure.
• TYPES OF SAMPLING

  1. Random sampling
  2. Simple random sampling
  3. Systematic random sampling
  4. Stratified random sampling
  5. Cluster sampling
• RANDOM SAMPLING

  • Every sample has an equal chance of being selected
• SIMPLE RANDOM SAMPLING

  • Most basic random sampling wherein each element in the population has an equal probability of being selected.
• SYSTEMATIC RANDOM SAMPLING

  • With a fixed periodic interval
  • Selecting every kth subject

  $k=$$\frac{N}{n}$
• STRATIFIED RANDOM SAMPLING

  • Population is divided into different strata or divisions.
  • Number of samples will be picked in each stratum
• CLUSTER SAMPLING

  • Population is divided into clusters or groups and then the clusters are randomly selected.
• PARAMETER

  • Measure used to describe the population.
  • Found using all data values in the population.
• STATISTIC

  • Measure that is used to describe the sample.
  • Found using the data values from the samples.
• PARAMETER

  • Population mean: myu
  • Population variance $\sigma^2$
  • Population standard deviation$\sigma$
• STATISTIC

  • Sample mean: x bar
  • Sample variance: $s^2$
  • Sample standard deviation:$s$