2.1 Statistical Sampling

• 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