2.1 Statistical Sampling

    Cards (52)

    • Random sampling ensures each member of the population has an equal chance of being selected.

      True
    • In stratified sampling, the population is divided into strata and samples are taken from each.
    • Arrange the benefits of statistical sampling in terms of their primary focus:
      1️⃣ Cost-effectiveness
      2️⃣ Time-saving
      3️⃣ Accuracy
      4️⃣ Feasibility
    • Systematic sampling involves selecting every nth member of the population.
    • Well-designed samples can provide accurate results.
    • Systematic sampling may introduce bias if the population has an underlying pattern.

      True
    • Statistical sampling allows researchers to make inferences about the entire population without examining every member.
    • Systematic sampling selects every nth member of the population.
    • Match the sampling method with its advantage:
      Random Sampling ↔️ Unbiased selection
      Systematic Sampling ↔️ Efficient implementation
      Stratified Sampling ↔️ Ensures subgroup representation
      Cluster Sampling ↔️ Cost-effective for dispersed populations
    • What does sample size affect in statistical analysis?
      Reliability and accuracy
    • Match the factor affecting sample size with its description:
      Population Size ↔️ Larger populations require larger samples
      Desired Precision ↔️ Higher precision requires larger samples
      Variability in Population ↔️ Greater variability needs larger samples
      Desired Confidence Level ↔️ Higher confidence needs larger samples
    • What are the primary benefits of statistical sampling?
      Cost-effectiveness and accuracy
    • Random sampling ensures that each member of the population has an equal chance of selection
    • Cluster sampling is particularly cost-effective when the population is geographically dispersed
    • Sample size refers to the number of observations or data points in a sample
    • Greater variability in the population requires a larger sample to capture
    • Systematic sampling error occurs when there is a bias in the sampling method
    • Biased sampling methods can introduce systematic errors
    • Stratified sampling ensures representation of different subgroups
    • Statistical sampling is the process of selecting a subset of a population to study.
    • Sampling is crucial in statistics because it allows researchers to study a smaller, manageable subset of a population.
      True
    • Match the sampling type with its description:
      Random sampling ↔️ Equal chance for each member
      Systematic sampling ↔️ Every nth member is selected
      Stratified sampling ↔️ Samples from each stratum
      Cluster sampling ↔️ Clusters within the population are selected
    • The primary purpose of statistical sampling is to obtain reliable data without examining every member of the population.
      True
    • Random sampling is the simplest and most unbiased sampling method.
    • Arrange the sampling methods from most unbiased to most biased:
      1️⃣ Random sampling
      2️⃣ Stratified sampling
      3️⃣ Cluster sampling
      4️⃣ Systematic sampling
    • Stratified sampling ensures representation of different subgroups in the population.

      True
    • Systematic sampling may introduce bias if the population has an underlying pattern.
    • Cluster sampling is more precise than stratified sampling.
      False
    • Larger populations generally require larger samples for accurate representation.
    • Statistical sampling involves studying a subset of a population.
    • Statistical sampling aims to provide accurate results while remaining cost-effective and feasible.
    • Systematic sampling is less random than true random sampling
      True
    • Factors to consider when choosing a sampling method
      1️⃣ Cost
      2️⃣ Time
      3️⃣ Bias
      4️⃣ Representativeness
    • What is one factor that affects sample size in statistical analysis?
      Population size
    • Why is appropriate sample size determination important in research?
      To ensure reliable results
    • How does sample size affect random sampling error?
      Larger sample reduces error
    • What is an example of random sampling in a real-world scenario?
      Surveying 500 random customers
    • When is cluster sampling particularly useful?
      Geographically dispersed populations
    • Match the sampling method with its key characteristic:
      Random sampling ↔️ Equal chance of selection
      Systematic sampling ↔️ Every nth member is selected
      Stratified sampling ↔️ Ensures subgroup representation
      Cluster sampling ↔️ Useful for dispersed populations
    • What is the defining characteristic of random sampling?
      Equal chance of selection