Think about what happens when you flip an ordinary coin one time. Can you predict with 100% accuracy what will happen? What if you flipped that coin 100 times? Would you be surprised that you would be pretty close if you had predicted the result to be tails 50 times and heads 50 times?

If you perform the same experiment (flipping a coin for example) enough times, the actual outcome will approach the expected one based on the probability of each event. This is a really great way of predicting outcomes when you are looking at a large number of trials or experiments. However, what happens when you try to predict the outcome of a single trial?

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Public health professionals make statements such as, “The United States’ 5-year survival rate for stage I breast cancer is 88%.” This number was calculated by looking at large numbers of women diagnosed with stage I breast cancer, then dividing the number of survivors at 5 years by the total number diagnosed. Numbers such as these allow public health professionals to compare current rates in the U.S. to rates in other countries or rates from previous years. It also allows for comparisons between survival rates of other types of cancers; however, it does not allow professionals to predict a specific individual’s chance of survival for 5 years.

For this Assignment, review the interactive media piece (video on Probability and Sampling) in this week’s Learning Resources. Reflect on the influence of sample size with respect to variability in a probability distribution. Then, consider the relationship between frequency and probability.

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**The Assignment: (1 page)**

- Explain why increasing the sample size decreased the variability in the interactive media piece.
- Explain how frequency is used to inform probability and why this important. Be sure to include the relevance of p-values as it concerns probability and the relationship with frequency distributions.