Respond to a colleague who posted about a different example from the one you chose to discuss. Respond in one or more of the ways listed below:

• Explain how the example your colleague chose could be used to demonstrate one additional feature of statistical probability not discussed in their initial post.
• A description of how the example your colleague chose strengthened your understanding of statistical probability.
• Ask a probing question about your colleague’s chosen probability demonstration or example and provide the foundation or rationale for the question.

Support your reply with at least one reference (textbook or other scholarly, empirical resources). You may state your opinion and/or provide personal examples; however, you must also back up your assertions with evidence (including in-text citations) from the source and provide a reference

This is the discussion post below that we need to respond to.

8 Real Life Examples Of Probability

For this discussion, the article I chose gives eight examples of probability with brief explanations. A couple of these examples, I never thought about as being probability or statistics such as elections or insurance risks. These examples demonstrate the concepts related to the statistical probability of random or chance events, gambler’s fallacy, and “over the long run.” All eight examples fall into the category of random or chance events because there is no way to control any of the outcomes, however one example “death in a car accident” although a car accident can not be predicted there are steps that you can take to prevent death, such as wearing a seatbelt, driving safely and within the speed limit as well as obeying traffic laws making these things reduce the probability of death in a car accident. Since probability is determined by what happens over the long run, the gambler’s fallacy claims it is victims, especially in cards or flipping a coin because they make the mistake of thinking since the coin landed on heads nine times so far then on the tenth time it must land on tails. The fallacy would be thinking that the coin must land on tails now (Heiman, 2015). Predicting probability in some scenarios is not always easy because, like with flipping a coin, there is a .50 chance of getting heads or tails. You could very well flip a coin 100 times, and it lands on heads 50% of the time. Or flip it 100 more times and it lands on heads 60% of the time. By reading the examples talking about insurance risks and elections, I was better able to understand probability because it shows how all three of the concepts relate to these. Insurance risks are determined by selecting samples of different age groups, sexes, family size, health, and overall level of responsibility. Using these can help you determine the probability of needing car insurance over health insurance and what would be the most beneficial. Insurance companies also use this information to determine what policy you need and the cost. They take into consideration random events and “over the long run” to determine if you are a risk for accident or health claims.

Heiman, G. (2015). Behavioral Sciences STAT (2nd ed.). Stamford, CT: Cengage.

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