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lesions, some of which turn into prostate cancer Half of these men were randomized to take 600 mg of green tea extract daily, while the other half were given a placebo pill The study was double-blind, neither the participants nor the doctors knew who was actually receiving green tea After one year, only 1 person taking green tea had gotten cancer, while 9 taking the placebo had gotten cancer Statistics: Unlocking the Power of Data Lock5Green Tea and Prostate Cancer A difference this large is unlikely to happen just by random chance. Can we conclude that green tea really does help prevent prostate cancer? (a) Yes (b) No Good randomized experiments allow conclusions about causality. Statistics: Unlocking the Power of Data Lock5Why not always randomize? Randomized experiments are ideal, but sometimes not ethical or possible Often, you have to do the best you can with data from observational studies Example: research for the Supreme Court case as to whether preferences for minorities in university admissions helps or hurts the minority students Statistics: Unlocking the Power of Data Lock5Was the sample randomly selected? Possible to generalize to the population Yes Should not generalize to the population No Was the explanatory variable randomly assigned? Possible to make conclusions about causality Yes Can not make conclusions about causality No Randomization in Data Collection Statistics: Unlocking the Power of Data Lock5DATA Two Fundamental Questions in Data Collection Population Sample Random sample??? Randomized experiment???
12/23/2012 8 Statistics: Unlocking the Power of Data Lock5Randomization Doing a randomized experiment on a random sample is ideal, but rarely achievable If the focus of the study is using a sample to estimate a statistic for the entire population, you need a random sample, but do not need a randomized experiment (example: election polling) If the focus of the study is establishing causality from one variable to another, you need a randomized experiment and can settle for a non-random sample (example: drug testing) Statistics: Unlocking the Power of Data Lock5Summary Association does not imply causation! In observational studies, confounding variables almost always exist, so causation cannot be established Randomized experiments involve randomly determining the level of the explanatory variable Randomized experiments prevent confounding variables, so causality can be inferred A control or comparison group is necessary The placebo effect exists, so a placebo and blinding should be used Statistics: Unlocking the Power of Data Lock5To Do Read Section 1.3 Complete the class survey (due TODAY) Do Homework 1 (due Tuesday, 9/11) If you haven’t already…Get the textbook (at bookstore tomorrow) Get a clicker and register it (due Tuesday, 9/11) Statistics: Unlocking the Power of Data Lock5