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An example where data were presented on an issue

Have you seen or read about an example where data were presented on an issue, but the sample was not representative of the population from which it was drawn? If you can’t recall a specific example, locate one using an Internet search.

For this discussion, summarize the issue and respond to the following questions.

Why do you believe the sample was not representative of the population?
What are the undesirable consequences of using a poor sampling technique?
How could the inaccurate reporting of the data been prevented?

Sample Answer

A Case Study: The Literary Digest Poll of 1936

The Issue: In 1936, the Literary Digest, a popular magazine at the time, conducted a massive poll to predict the outcome of the U.S. presidential election between incumbent Franklin D. Roosevelt and Republican challenger Alf Landon. The poll famously predicted a landslide victory for Landon, but Roosevelt won by a significant margin.

Why the Sample Was Not Representative: The primary issue with the Literary Digest poll was its sampling methodology. The magazine sent out millions of ballots to individuals whose names were drawn from telephone directories and automobile registration lists. This method inherently biased the sample towards wealthier individuals who were more likely to own cars and telephones. As a result, the poll overrepresented Republicans, who tended to be wealthier at the time, and underestimated the support for Roosevelt, who was popular among lower-income voters.

Full Answer Section

Undesirable Consequences of a Poor Sampling Technique:

Inaccurate Predictions: The most obvious consequence is the failure to accurately predict the outcome of the election. This can lead to significant miscalculations and incorrect decision-making.
Erosion of Public Trust: When polls are consistently wrong, it can erode public trust in polling and data-driven decision-making.
Misallocation of Resources: Inaccurate predictions can lead to misallocation of resources, such as campaign funds or advertising budgets.
Policy Mistakes: In the realm of public policy, inaccurate data can lead to ineffective or harmful policies.

How to Prevent Inaccurate Reporting: To prevent inaccurate reporting of data, it is crucial to employ sound sampling techniques. Here are some key strategies:

Random Sampling: This involves selecting individuals randomly from the population to ensure that each member has an equal chance of being included.
Stratified Sampling: This technique divides the population into subgroups based on relevant characteristics (e.g., age, gender, income) and then samples from each subgroup to ensure representation.
Cluster Sampling: This involves dividing the population into clusters (e.g., geographic areas) and then randomly selecting clusters to sample.
Careful Sample Size Determination: The sample size should be large enough to accurately represent the population.
Validation and Verification: Data should be carefully validated and verified to ensure accuracy and reliability.

By adhering to these principles, researchers can significantly improve the accuracy and reliability of their findings.

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An example where data were presented on an issue

Sample Answer

A Case Study: The Literary Digest Poll of 1936

Full Answer Section

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