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A study reported in the May 17,2012 , issue of The New England Journal of Medicine followed people for an average of 13 years and found that people who consumed two or more cups of coffee a day were less likely to die during the course of the study than those who drank no coffee. a. Was this study a randomized experiment or an observational study? Explain how you know. b. Based on this study, can it be concluded that drinking coffee causes people to live longer? c. The following headlines appeared on news websites reporting these results. In each case, explain whether or not the conclusion in the headline is justified. i. "Coffee positively associated with life expectancy" (Source: http://www.coffeeandhealth.org/2012/05/21/coffee-positively-associated-with- life-expectancy/ ) ii. "NIH Study: Coffee Really Does Make You Live Longer, After All" (Source: http://www.theatlantic.com/health/archive/2012/05/nih-study-coffee-really- doesmake-you-live-longer-after-all/257302/)

Step by step solution

01

Identify Study Type

Determine if the study is a randomized experiment or an observational study. A randomized experiment requires the random assignment of subjects to different groups, typically a treatment group and a control group, to minimize biases and establish causation. An observational study, in contrast, involves observing subjects in their natural settings without any imposed interventions. In this case, the study simply followed people and observed their coffee consumption without assigning coffee drinkers and non-drinkers, thus it is an observational study.

02

Analyze Causation from Observational Study

Consider whether the study can establish causation. Observational studies can find associations or correlations, but they cannot prove causality due to potential confounding variables. Since this study is observational, it suggests an association between drinking coffee and lower likelihood of death but cannot conclusively establish that drinking coffee causes people to live longer.

03

Evaluate Headline 1

Assess the justification of the headline: "Coffee positively associated with life expectancy." Since the headline uses 'associated,' which implies a correlation, it is justified given the nature of observational studies that can only identify associations, not causation. Hence, the headline accurately reflects the findings of the study without implying causation.

04

Evaluate Headline 2

Assess the justification of the headline: "NIH Study: Coffee Really Does Make You Live Longer, After All." This headline implies causation by claiming that coffee makes people live longer. Since the study is observational and cannot establish a causal relationship, this headline is not justified as it overstates the study’s findings.

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Key Concepts

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

Causality in Statistics

Causality refers to the relationship between two events where one event causes the other. Establishing causality in statistics is crucial as it helps determine the true effect of one variable on another. However, proving causality can be complex.
In the context of our coffee study, causality would mean proving that drinking coffee directly affects and increases life expectancy.
The study, however, does not prove this because it lacks critical elements that would establish a causal link.

• There could be many other factors at play, known as confounding variables, that might contribute to the observed outcome.
• Without random assignment and control, causation can't be claimed.
• Thus, it is more fitting to say there's an association, rather than a direct cause and effect.

Understanding the difference between correlation and causation is essential, as it prevents overinterpretation of data from studies.

Randomized Experiment

A randomized experiment is a type of study designed to establish cause-and-effect relationships. It involves randomly assigning participants into two or more groups to receive different treatments or conditions. This methodology helps eliminate bias and controls for confounding factors.
In a randomized experiment, one group might receive the treatment, like an increased coffee intake, while another group receives a placebo or no treatment. This setup helps in confidently identifying causal relationships.
However, the study on coffee consumption and life expectancy did not utilize a randomized design.

• Participants were not told to drink coffee or not; they were merely observed in their natural coffee-drinking habits.
• No control group was introduced to compare against those who drank coffee.

Thus, the study falls under the category of observational research, which limits its capacity to identify causality.

Confounding Variables

Confounding variables are extraneous factors that can affect the outcome of a study, making it challenging to determine a clear causal link between variables of interest. These variables might create a false impression of a relationship or mask one that exists.
In our discussed study, factors such as diet, exercise, socioeconomic status, and health conditions are potential confounders. For example:

• Individuals who regularly consume coffee might also engage in other health-promoting behaviors like exercising regularly, which independently affect life expectancy.
• The study's design didn't control these confounding variables, hence the observed association between coffee consumption and reduced mortality might be influenced by these unaccounted factors.

For this reason, researchers are cautious about making causal claims from observational studies due to the potential interference from confounders.

Statistical Association

Statistical association refers to a connection or relationship between two or more variables within a dataset. However, it's important to note that such associations do not imply causation.
In our coffee consumption study, there's a statistical association between drinking coffee and lower mortality rates. This means there's a noticeable pattern or correlation in the data showing that coffee drinkers had a lower likelihood of dying during the study period.

• This finding is valuable for generating insights, hypotheses, and further research questions.
• However, researchers must be careful not to interpret it as direct evidence that coffee consumption is beneficial, as the study design does not support a causal conclusion.

Understanding statistical associations is a foundational skill in research that helps guide further exploration, but always with an awareness of its limitations.