3.3 Random Sampling and Data Collection

• One benefit of random sampling is that it ensures the sample is unbiased
• The purpose of random sampling is to ensure the sample accurately represents the entire population
• What is the primary characteristic of simple random sampling?

  Equal chance for all
• Which random sampling method ensures representation of key subgroups within the population?

  Stratified random sampling
• Match the random sampling method with its description:

  Simple Random Sampling ↔️ Each member has equal chance of selection

  Stratified Random Sampling ↔️ Population divided into subgroups

  Cluster Random Sampling ↔️ Entire clusters are randomly selected

  Systematic Random Sampling ↔️ Sample selected at regular intervals
• Cluster random sampling is useful when the population is geographically dispersed
• Observations systematically record information about behaviors or events
• Response bias can happen due to social desirability or recall issues.


  True
• Random sampling ensures each member of the population has an equal chance
• What is the primary goal of random sampling?

  Represent the population
• Simple random sampling is the most basic form of random sampling.


  True
• The choice of random sampling method depends on factors like population size, availability of population lists, and the need to ensure representation of specific subgroups
• What is sampling bias?

  Unrepresentative sample of population
• What is the purpose of pilot testing in data collection?

  Refine questions and identify issues
• What is random sampling in statistics?

  Equal chance for all
• Match the benefit of random sampling with its explanation:

  Unbiased ↔️ Prevents influence by personal preferences

  Representative ↔️ Accurately reflects population characteristics

  Generalizable ↔️ Allows findings to extend to the population
• Random sampling prevents personal preferences or assumptions from influencing sample selection.

  True
• In stratified random sampling, the population is divided into strata
• Match the random sampling method with its description:

  Simple Random ↔️ Each member has an equal chance

  Systematic ↔️ Sample at regular intervals

  Stratified ↔️ Division into strata

  Cluster ↔️ Randomly select entire clusters
• In simple random sampling, each member of the population has an equal chance of being selected
• Surveys involve gathering information using questionnaires or interviews
• Match the bias type with its description:

  Sampling Bias ↔️ Sample does not represent population

  Response Bias ↔️ Participants provide inaccurate responses

  Observational Bias ↔️ Data collection influences observed behavior
• Observational bias occurs when the data collection process influences observed behavior
• A representative sample allows results to be generalizable
• Random sampling ensures the sample is unbiased, representative, and allows findings to be generalizable
• Match the data collection technique with its description:

  Surveys ↔️ Gather information using questionnaires

  Experiments ↔️ Manipulate variables to observe outcomes

  Observations ↔️ Record behaviors or events

  Secondary Data ↔️ Analyze data collected for another purpose
• Response bias occurs when participants provide inaccurate or incomplete responses
• Ensuring informed consent is a key ethical consideration in data collection.


  True
• Random sampling ensures that a sample is unbiased and representative of the population.


  True
• In a class of 50 students, if 10 are randomly selected, what is the probability that each student is chosen?

  1/5
• Order the following random sampling methods from simplest to most complex:

  1️⃣ Simple Random Sampling

  2️⃣ Systematic Random Sampling

  3️⃣ Stratified Random Sampling

  4️⃣ Cluster Random Sampling
• Systematic random sampling requires a complete list of the population to ensure equal intervals.


  True
• Systematic random sampling selects the sample at regular intervals
• Stratified random sampling ensures representation of key subgroups within the population.

  True
• Experiments manipulate variables to observe their effect on an outcome.


  True
• Sampling bias occurs when the sample does not accurately represent the entire population
• Match the random sampling method with its key feature:

  Simple Random ↔️ All members have equal chances

  Stratified Random ↔️ Population divided into subgroups

  Systematic Random ↔️ Members selected at regular intervals
• Match the benefit of random sampling with its explanation:

  Unbiased ↔️ Prevents systematic errors

  Representative ↔️ Reflects population characteristics

  Generalizable ↔️ Results apply to entire population
• Match the random sampling method with its description:

  Simple Random Sampling ↔️ Each member has an equal chance of selection

  Stratified Random Sampling ↔️ Population divided into strata

  Systematic Random Sampling ↔️ Sample selected at regular intervals

  Cluster Random Sampling ↔️ Entire clusters are randomly selected
• What does stratified random sampling ensure?

  Representation of subgroups