This question evaluates a researcher's claim that raising income will increase life expectancy, based on data from 15 countries showing higher income linked to longer expectancy. The data indicate a positive association across countries, but it's observational. Choice A is most justified as it reports the association without proving causation. Choice B oversteps by claiming income is the only cause. Choice D wrongly equates country averages to individual effects. A key strategy is to distinguish cross-sectional associations from causal proof, considering other national factors.

This question asks which conclusion is most appropriate from an observational survey showing a positive association between sleep and math scores in a volunteer sample of 120 students. The data indicate that, in this group, students reporting more sleep tended to have higher scores, but the study was not experimental and relied on self-selected participants. Choice B is supported because it accurately recognizes the association without claiming causation or overgeneralizing, emphasizing that the results do not prove increasing sleep will raise scores for any individual. In contrast, choice A oversteps the evidence by asserting causation, ignoring potential confounders like study habits that could explain both more sleep and higher scores. Similarly, choice D incorrectly assumes the small volunteer sample represents all students nationwide. When evaluating claims from surveys, always distinguish between observed associations in a specific sample and unproven causal effects or broad generalizations.

This question evaluates which statement is best supported by a randomized experiment comparing weight changes from sugar-sweetened beverages versus water over 6 weeks in 80 adults. The data show the sugar drink group gained an average of 2.1 lb, while the water group lost 0.3 lb, with random assignment to groups. Choice A is correct because random assignment minimizes confounders, allowing a causal inference about the beverage's effect on weight within this study's participants. However, choice B oversteps by claiming the results apply to any time period like 5 years, ignoring that the study was limited to 6 weeks. Choice D mistakenly treats the experiment as merely correlational, disregarding the strength of randomization for causation. A key test-taking strategy is to recognize that well-designed experiments with random assignment can support causal claims, but only within the study's scope and conditions.

The question identifies the limitation that most weakens a headline's causal claim about bike commuting increasing traffic accidents, based on monthly city data showing a positive association over one year. The data reveal that months with more bike commuters had more accidents, but this is observational and aggregated by month. Choice A is supported as it highlights potential confounders like weather or traffic volume that could drive both variables, preventing a causal interpretation. In contrast, choice B oversteps by assuming the sample size eliminates confounders, which it does not in observational data. Choice D incorrectly assumes that because both are counts, the relationship is causal and linear. Remember, when assessing causal headlines from correlations, look for unaccounted confounders that could explain the pattern without causation.

This question assesses which statement is most justified from comparing final exam averages between two existing classes using different homework methods, with online users averaging 84 and paper users averaging 78. The data show an association where the online class had higher scores, but classes were not randomly assigned. Choice B is appropriate because it acknowledges the association while noting that without randomization, pre-existing differences like student ability could confound results. Choice A oversteps by claiming causation, as the observational design cannot rule out other explanations. Choice D wrongly assumes the average difference applies exactly to every student. A useful strategy is to differentiate observational comparisons from experiments; the former suggest associations but not proven causes.

The question determines which claim is best supported by a survey of 300 early respondents showing a negative correlation between exercise hours and self-rated stress among university students. The data indicate that in this sample, more exercise was linked to lower stress, but the survey used non-random early responses. Choice B is justified as it reports the association without inferring causation or ignoring potential response bias. However, choice A oversteps by claiming causation from a correlation in observational data. Choice D incorrectly assumes the sample represents the entire population despite the biased collection method. When analyzing survey results, carefully note sampling limitations and avoid equating correlation with causation.

This question critiques a manager's conclusion that a training program increasing average sales from $18,000 to $21,000 in 25 volunteers will boost sales for all company employees. The data show an average increase post-training in this small volunteer group, but without a control or randomization. Choice A is most appropriate as it points out overgeneralization from a non-representative sample limited to one department. In contrast, choice B oversteps by assuming the increase must be causal and universal, ignoring self-selection bias. Choice D wrongly claims the average proves exact individual increases. A strategy for such questions is to evaluate if the sample's characteristics limit broader inferences, emphasizing data shows patterns but not guaranteed effects everywhere.

The question asks for the most appropriate conclusion from a randomized experiment where students assigned to music read faster on average (255 words/min) than those in silence (240 words/min). The data demonstrate a difference in means favoring music, with random assignment controlling for confounders. Choice A is supported because randomization enables a causal claim about music's effect on reading speed in this group under the study's conditions. However, choice B oversteps by guaranteeing effects for every student, as averages do not imply uniform individual outcomes. Choice C mistakenly dismisses the experimental design's ability to infer causation. When interpreting experiments, distinguish supported causal effects within the study from unproven universal or individual guarantees.

This question evaluates a claim that building more parks will reduce obesity, based on observational data from 10 neighborhoods showing lower obesity in those with more parks per square mile. The data suggest an association between park density and obesity rates, but the study is not experimental. Choice B is best as it notes the consistency with the claim while cautioning about confounders like income or walkability. Choice A oversteps by equating the observational comparison to a randomized experiment, which it is not. Choice D wrongly assumes the small sample eliminates alternatives. A key strategy is to remember that associations support hypotheses but require experiments to confirm causation, always considering confounders.

The question assesses what the evidence from a study of 60 students showing a negative correlation between screen time and GPA justifies about a student's claim that reducing screen time will raise their GPA. The data reveal an association where lower screen time links to higher GPA in the sample, but it's observational. Choice B is most justified as it reports the association without establishing causation for individuals. In contrast, choice A oversteps by claiming the correlation proves causation. Choice D incorrectly assumes correlation means exact matching. When evaluating personal applications, stress that data show group patterns but not guaranteed individual causal effects without further evidence.