Which question can be answered using inferential statistics?

Which question can be answered using inferential statistics?

• A. How many total subscribers do we currently have?
• B. What percentage of our subscribers pay for their subscription using a credit card?
• C. Which of two cover designs will result in a higher percentage of non-subscriber sales?
• D. Has the number of subscribers to our publication increased or decreased over the last two years?

Answer: (C)

Inferential statistics are used to make predictions or inferences about a population based on a sample of data. This means that they allow researchers to draw conclusions that extend beyond the immediate data collected. In the context provided, the question about which of the two cover designs will result in a higher percentage of non-subscriber sales is a classic example of inferential statistics at work. This type of analysis typically involves hypothesis testing or experiments, where samples of the population (non-subscribers in this case) would be used to determine the effectiveness of the two different cover designs in a broader market. By conducting a study or survey involving a representative sample of non-subscribers, one can apply statistical methods to hypothesize about the sales impact of each design. The results can then inform decisions with the confidence that the findings can be extrapolated to a larger audience. In contrast, the other options either request descriptive measurements (like total counts or percentages from the current data), which do not infer broader conclusions, or ask about changes over time based on existing data. These do not typically require the use of inferential statistics, which are aimed at exploring or predicting trends and differences based on sampled data.

Inferential statistics are used to make predictions or inferences about a population based on a sample of data. This means that they allow researchers to draw conclusions that extend beyond the immediate data collected.

In the context provided, the question about which of the two cover designs will result in a higher percentage of non-subscriber sales is a classic example of inferential statistics at work. This type of analysis typically involves hypothesis testing or experiments, where samples of the population (non-subscribers in this case) would be used to determine the effectiveness of the two different cover designs in a broader market.

By conducting a study or survey involving a representative sample of non-subscribers, one can apply statistical methods to hypothesize about the sales impact of each design. The results can then inform decisions with the confidence that the findings can be extrapolated to a larger audience.

In contrast, the other options either request descriptive measurements (like total counts or percentages from the current data), which do not infer broader conclusions, or ask about changes over time based on existing data. These do not typically require the use of inferential statistics, which are aimed at exploring or predicting trends and differences based on sampled data.