High School

A nutritionist suspects there is a difference between the flavor distributions in two brands of berry snack packs. He selects a random sample of berry snack packs from Brand A and Brand B and records the amount of each type of berry. The results are shown in the table.

[tex]
\[
\begin{tabular}{|c|c|c|c|}
\hline
Brand & \text{Strawberry} & \text{Raspberry} & \text{Blueberry} \\
\hline
A & 8 & 10 & 5 \\
\hline
B & 4 & 14 & 3 \\
\hline
\end{tabular}
\]
[/tex]

Are the conditions for inference met?

A. No, the data do not come from a random sample.
B. No, the [tex]$10\%$[/tex] condition is not met.
C. No, the Large Count condition is not met since all expected counts are not greater than 5.
D. All conditions for inference are met.

Answer :

To determine if the conditions for inference are met, we need to check a few important criteria. This typically involves understanding if the data gathered are suitable for a statistical test, like the Chi-Square test. We have:

1. Random Sample: The problem specifies that a random sample was selected, so we presume this condition is met.

2. 10% Condition: This condition checks that the sample size is less than 10% of the total population. However, we do not have enough information about the total population size to make this determination, so we cannot check this condition.

3. Large Count Condition: This condition requires that all expected counts in a contingency table are greater than 5. If the expected counts are too small, the results of a Chi-Square test may not be reliable.

Based on the calculation, which shows observed berry counts and uses them to compute expected counts, the Large Count condition is not met because not all expected counts are greater than 5. This means the Chi-Square test could be unreliable for this data.

Therefore, the conclusion is: No, the Large Count condition is not met since all expected counts are not greater than 5.