Population Sampling

What is simple random sampling?

• Method of selecting a sample from a population where each individual has a different chance of being chosen.

• Method of selecting a sample from a population where only certain individuals are chosen.

• Method of selecting a sample from a population where each individual is chosen multiple times.

• Method of selecting a sample from a population where each individual has an equal chance of being chosen.

Why is simple random sampling considered a fair method of sampling?

• Every member of the population has an equal chance of being selected.

• Only a small portion of the population is sampled.

• The sample is representative of the population.

• The sampling process is quick and efficient.

What is the formula to calculate the sample size for simple random sampling?

• n = (Z^2 * p * (1-p)) / (E^2) + 1

• n = (Z^2 * p * (1-p)) / (E^2) - 1

• n = (Z^2 * p * (1-p)) / (E^2) * 2

• n = (Z^2 * p * (1-p)) / (E^2)

What are the advantages of simple random sampling?

• Simple random sampling is not suitable for large populations.

• Simple random sampling has several advantages.

• Simple random sampling does not provide representative samples.

• Simple random sampling is time-consuming and expensive.

What are the disadvantages of simple random sampling?

• Potential bias, lack of representation, difficulty in obtaining a truly random sample

• Limited generalizability, time-consuming process, high cost

How can you ensure randomness in simple random sampling?

• Use a random number generator or a random selection method.

• Use a predetermined list of participants.

• Select participants based on a specific characteristic.

• Choose participants alphabetically.

What is the difference between simple random sampling and stratified random sampling?

• Simple random sampling divides the population into subgroups and selects random samples from each subgroup, while stratified random sampling selects individuals randomly from the entire population.

• Simple random sampling selects individuals based on specific characteristics, while stratified random sampling selects individuals randomly.

• Simple random sampling and stratified random sampling are the same and can be used interchangeably.

• The difference between simple random sampling and stratified random sampling is that simple random sampling selects individuals randomly from the entire population, while stratified random sampling divides the population into subgroups and selects random samples from each subgroup.

When is simple random sampling most appropriate to use?

• When the population is large and homogeneous

• When the population is small and homogeneous

• When the population is large and heterogeneous

• When the population is small and heterogeneous

What are some examples of simple random sampling in real-life situations?

• Picking a random sample of books from a library for a reading club, selecting a group of customers to participate in a product testing study, or randomly selecting houses from a neighborhood for a housing survey.

• Choosing a sample of friends to ask for their opinion on a movie, selecting a group of employees to participate in a workplace satisfaction survey, or randomly selecting cars on a highway for a traffic study.

• Selecting a random sample of students from a school to conduct a survey, randomly selecting households from a city for a market research study, or randomly selecting patients from a hospital for a medical research study.

• Selecting a random sample of students from a school to conduct a survey, randomly selecting households from a city for a market research study, or randomly selecting patients from a hospital for a medical research study.

What are the potential sources of bias in simple random sampling?

• Undercoverage and nonresponse bias

• Volunteer bias and convenience bias

• Sampling bias and measurement bias

• Selection bias and response bias