Intro to Marketing Research: Sampling Sampling Error vs NonSampling Error

What It Is

Sampling Error vs Non-Sampling Error refers to the difference between errors that occur due to the sample selection process and those that occur due to other factors. A classic example is the 1936 Literary Digest presidential poll, which incorrectly predicted Alf Landon's victory over Franklin D. Roosevelt. The error was due to a sampling bias, where the poll included a disproportionate number of wealthy individuals who were more likely to vote for Landon. This matters for marketing decision-making because it highlights the importance of understanding the potential sources of error in research findings.

Key Terms & Concepts

• Sampling Error: The error that occurs due to the sample selection process, which can lead to biased or inaccurate results.
  • Example: A study on customer satisfaction that only surveys customers who have made a purchase in the past month may miss the opinions of customers who have not made a purchase recently.
• Non-Sampling Error: The error that occurs due to factors other than the sample selection process, such as measurement error, data entry errors, or interviewer bias.
  • Example: A study on customer satisfaction that uses a flawed survey instrument may produce inaccurate results due to the measurement error.
• Sampling Bias: A type of sampling error that occurs when the sample is not representative of the population, leading to biased results.
  • Example: A study on customer demographics that only surveys customers who are online may miss the opinions of customers who do not have access to the internet.
• Non-Response Bias: A type of sampling error that occurs when some members of the population do not respond to the survey, leading to biased results.
  • Example: A study on customer satisfaction that only surveys customers who respond to the survey may miss the opinions of customers who do not respond.
• Margin of Error: The maximum amount by which the sample estimate may differ from the true population parameter.
  • Formula: Margin of Error = (Z * (σ / √n)), where Z is the Z-score, σ is the standard deviation, and n is the sample size.
• Confidence Interval: A range of values within which the true population parameter is likely to lie.
  • Formula: Confidence Interval = Sample Estimate ± (Margin of Error)
• Type I Error: The error of rejecting a true null hypothesis.
  • Example: A study on customer satisfaction that concludes that a new product is more popular than an existing product when, in fact, there is no difference.
• Type II Error: The error of failing to reject a false null hypothesis.
  • Example: A study on customer satisfaction that concludes that a new product is not more popular than an existing product when, in fact, it is more popular.
• Reliability: The consistency of a measure or instrument over time or across different samples.
  • Example: A study on customer satisfaction that uses a survey instrument that is reliable will produce consistent results over time.
• Validity: The accuracy of a measure or instrument in measuring what it is supposed to measure.
  • Example: A study on customer satisfaction that uses a survey instrument that is valid will produce results that accurately reflect customer opinions.

Common Misunderstandings

• Misunderstanding: Sampling error is the only type of error that occurs in research studies.
• Correction: Non-sampling error can also occur in research studies due to factors such as measurement error, data entry errors, or interviewer bias.
• Misunderstanding: A large sample size always reduces the margin of error.
• Correction: While a large sample size can reduce the margin of error, it is not the only factor that affects the margin of error. The standard deviation of the population and the Z-score also play a role.
• Misunderstanding: A confidence interval is the same as a margin of error.
• Correction: A confidence interval is a range of values within which the true population parameter is likely to lie, while a margin of error is the maximum amount by which the sample estimate may differ from the true population parameter.

Quick Application / Identification

Scenario: A marketing researcher wants to estimate the average amount spent by customers on a new product. The researcher selects a sample of 100 customers and estimates the average amount spent to be $50. However, the researcher is unsure of the margin of error. What is the next step the researcher should take?

Answer: The researcher should calculate the margin of error using the formula Margin of Error = (Z * (σ / √n)), where Z is the Z-score, σ is the standard deviation, and n is the sample size.

Explanation: The researcher needs to calculate the margin of error to determine the range of values within which the true population parameter is likely to lie.

Last-Minute Revision

• Sampling Error is the error that occurs due to the sample selection process. ⚠️
• Non-Sampling Error is the error that occurs due to factors other than the sample selection process.
• Sampling Bias occurs when the sample is not representative of the population.
• Non-Response Bias occurs when some members of the population do not respond to the survey.
• Margin of Error is the maximum amount by which the sample estimate may differ from the true population parameter.
• Confidence Interval is a range of values within which the true population parameter is likely to lie.
• Type I Error is the error of rejecting a true null hypothesis.
• Type II Error is the error of failing to reject a false null hypothesis.
• Reliability is the consistency of a measure or instrument over time or across different samples.
• Validity is the accuracy of a measure or instrument in measuring what it is supposed to measure.
• Z-score is a statistical measure used to calculate the margin of error.
• σ (sigma) is the standard deviation of the population.
• n is the sample size.
• Cronbach's alpha is a measure of reliability.
• Regression equation is a statistical model used to predict a continuous outcome variable.