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# Topic Often times there are so many variables involved in a study that it is dif

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Topic

Often times there are so many variables involved in a study that it is difficult to distinguish “causes”. For example, there is the belief that “smoking causes lung cancer”. I tend to believe this myself but my own mother was a chain smoker and never developed lung cancer. There are many other variables that might affect the outcome of lung cancer such as working conditions, heredity, diet, etc.

It is too hard to account for every “factor” in these circumstances so we might just focus on one such as studying lung cancer patients and asking them if they smoked or not.

In this discussion, you are going to come up with some example of the binomial probability distribution. Remember our definition:

Def: Binomial probability distribution

1. procedure has fixed # of trials

2. trials must be independent

3. each trial must have all outcomes classified into 2 categories

4. probabilities must remain constant for each trial

For example, we interview 50 lung cancer patients (fixed # of trials). They come from all occupations and parts of the state (independence). Either they smoked or did not smoke (2 categories). If 15% of the population smokes then the probability would remain constant for each trial equaling 15% or 0.15. This would meet the conditions for a binomial probability distribution.

Come up with your own examples of a binomial probability distribution. It can be made up, you do not need actual numbers.

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