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An article in the journal \(B M C\) Medicine reported on a study designed to study the effect of diet on depression. Subjects suffering from moderate to severe depression were randomly assigned to one of two groups: a diet intervention group and a social support control group. The 33 subjects in the diet intervention group received counseling and support to adhere to a "ModiMedDiet," based primarily on a Mediterranean diet. The 34 subjects in the social support group participated in a "befriending" protocol, where trained personnel engaged in conversation and activities with participants. At the end of a 12 -week period, 11 of the diet intervention group achieved remission from depression compared to 3 of the control group. \begin{tabular}{|l|c|c|} \hline & Diet (Intervention) & Support (Control) \\ \hline Remission & 11 & 3 \\ \hline No Remission & 22 & 31 \\ \hline \end{tabular} a. Find and compare the sample percentage of remission for each group. b. Was this a controlled experiment or an observational study? Explain. c. Can we conclude that the diet caused a remission in depression? Why of why not?

Step by step solution

01

Calculate Sample Percentage of Remission for Each Group

Calculate the percentage of remission for each group using the formula: \((\text{Number of success} / \text{Total number}) * 100\). For the 'Diet Intervention' group, the calculation would be: \((11 / 33) * 100 = 33.33\%\). And for the 'Support Control' group: \((3 / 34) * 100 = 8.82\%\)

02

Determine Type of the Study

This was a controlled experiment as subjects were randomly assigned to one of the two groups, and the conditions were carefully controlled by the experimenters.

03

Make a Conclusion

While the data suggests that the diet intervention group had a higher rate of remission, it doesn't necessarily mean it caused the remission. The remission could be due to other factors. This is a common concept in statistics referred to as 'correlation does not imply causation'.

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Key Concepts

These are the key concepts you need to understand to accurately answer the question.

Sample Percentage Calculation

Understanding sample percentages is integral to interpreting data in controlled experiments and studies. To calculate the sample percentage of a certain outcome, you'd use the simple formula: \(\text{Sample Percentage} = \frac{\text{Number of occurrences of the outcome}}{\text{Total number of cases in the sample}} \times 100\). For example, in the study mentioned, the sample percentage of remission in the diet intervention group is calculated by dividing the number of successes by the total number in the group and then multiplying by 100, leading to: \(\frac{11}{33} \times 100 = 33.33\%\).

Similarly for the support control group, the sample percentage is \(\frac{3}{34} \times 100 = 8.82\%\). These percentages facilitate a straightforward comparison: 33.33% of the diet group experienced remission versus 8.82% of the control group. Such calculations are essential for accurately assessing the effect of the variable being tested—in this case, the dietary intervention versus social support.

Random Assignment

Random assignment is a critical process in conducting controlled experiments, as it ensures each participant has an equal chance of being placed in either the intervention or control group. This is crucial for minimizing biases and establishing causality. In the given study, individuals were randomly assigned to the diet intervention group or the social support control group.

Random assignment facilitates the creation of comparable groups, which means any differences observed in the outcome—like the higher remission rate in the diet group—can more confidently be attributed to the intervention itself rather than pre-existing differences between subjects. This is the backbone of experimental design and helps establish a causal link between the intervention and the observed outcomes.

Correlation Does Not Imply Causation

It's essential to distinguish between correlation and causation, a common concept in statistics and research. A correlation indicates a relationship or association between two variables, whereas causation implies that one variable actually causes the change in the other. Just because two things appear to be related does not mean that one causes the other.

In the context of the study, even though there seems to be a higher remission rate in the diet group compared to the control group, it isn't definitive proof that the diet caused the remission. There could be other factors at play such as the participants' lifestyle, medical history, or even placebo effects. To establish causation, researchers need to control for other variables and often conduct multiple studies to reinforce their findings. Recognizing that 'correlation does not imply causation' is fundamental to accurately interpreting research and avoiding misleading conclusions.