# Participants nor the doctors knew who was actually

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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
Green 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?
Statistics: Unlocking the Power of Data Lock 5
Statistics: Unlocking the Power of Data Lock 5 Types of Randomized Experiments Randomizing cases into different treatment groups is called a randomized comparative experiment We can also give each treatment to each case, and just randomize the order in which treatments are received: matched pairs experiment Either are valid randomized experiments!
Statistics: Unlocking the Power of Data Lock 5 Type of Randomization Run an experiment using 30 right-handed people to determine whether gripping strength is greater in the dominant hand. a) Randomized comparative design – randomly divide the 30 people into two groups of 15 each b) Matched pairs design – randomize the order by selecting 15 to first use the tight hand and the other 15 to first use the left and examine the difference between the two values.
Statistics: Unlocking the Power of Data Lock 5 Was 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 Lock 5 DATA Two Fundamental Questions in Data Collection Population Sample Random sample??? Randomized experiment???
Statistics: Unlocking the Power of Data Lock 5 Randomization 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 Lock 5 Summary 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