Sampling and Confidence Intervals

A market researcher defines a group of 10,000 customers for a study. What is this group called?

• Sample

• Population

• Parameter

• Statistic

An analyst selects 250 customers randomly from a database of 10,000 for a survey. This group is the:

• Population

• Confidence interval

• Sample

• Sampling error

The difference between the sample mean and the population mean is known as:

• Measurement bias

• Sampling error

• Non-sampling error

• Confidence level

Which of the following best defines a 'confidence interval'?

• A guess of the population mean

• A range of values likely to contain the population parameter

• The variability within the sample

• A summary of the sampling procedure

A manager claims, "We are 95% confident that the average inventory error is between 4 and 8 units." This means:

• 95% of the inventory errors lie between 4 and 8 units

• The population mean is 6

• There is a 95% chance the true mean lies within 4-8

• If we repeated the sampling process, 95% of intervals would contain the true mean

Why is it important to create representative samples?

• To ensure all responses are positive

• To eliminate nonresponse bias

• To generalize results accurately to the population

• To reduce the number of participants

If a sample only includes customers from urban areas when the population includes rural areas too, what is the issue?

• Over-sampling

• Selection bias

• Random error

• Stratified sampling

Which of the following reduces the risk of an unrepresentative sample?

• Convenience sampling

• Judgment sampling

• Stratified random sampling

• Snowball sampling

A poorly drawn sample can affect reliability by:

• Increasing the confidence level

• Decreasing sampling error

• Leading to biased conclusions

• Improving data accuracy

Which of the following does NOT increase reliability of statistical results?

• Random sampling

• Increasing sample size

• Using only volunteers

• Stratified sampling