Lecture 3 Articles.docx

# Example 3 study about what of population consider

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Example 3 – study about what % of population consider themselves to be liberal, happy, etc o Based on General Social Survey (GSS) – survey on societal trends conducted every other year Goal of a study is to select a sample representative of the population w/ a random sample o Random sample – using probability-based method to select individual for a sample from a population o Most polls use probability-based sampling methods to select individuals from representative panels Margin of error – expected amount of random variation in statistics, often defined for 95% CI o Key to margin of error is that we can make claims about how often a sample result would fall within a certain distance from the unknown population value by chance alone Non-random samples are often suspect to bias – overrepresents & underrepresents some segments of population Primary question of interest in many studies is about differences between groups Example 4 – study on whether people display more creativity when thinking about intrinsic or extrinsic motivations o Subjects answered survey questions about intrinsic or extrinsic motivations & asked to write a haiku Not always the case that those w/ extrinsic motivations had higher creativity Achilles heel of human cognition – difficulties w/ thinking about probabilistic tendencies o Comparing only the means of the 2 groups fails to consider variability of creativity scores in the groups o Tendency for creativity scores to be higher in intrinsic group, but difference is not large o P-value is very small at 0.002 – focusing on intrinsic motivations tends to increase creativity scores Random assignment tends to balance out all variables related to the independent variable o Should produce groups that are as similar as possible except for variable you are studying

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Statistical thinking involves careful design of a study to collect data to answer a focused research question o Random assignment is essential to generalize results from our sample to a larger population o Random assignment is key to drawing cause-and-effect conclusions Probability models help us assess how much random variation we can expect in our results Coffee study was a 14-year study w/ fairly large sample sizes o P values were small even thought % reduction in risk was not extremely large o Observational study – no cause and effect conclusions can be drawn btwn coffee drinking & longevity Studies should be reviewed in larger context of similar studies and consistency of results across studies w/ constant caution that it was not a randomized experiment
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