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Discussion: Probability in Real Life
People use probability to make many decisions. We must remember, however, that statistical probability deals only with random events, or things that happen by chance. For example, flipping a fair coin is a random event because each side has an equal likelihood of landing facing up. When faced with making a conscious choice based on probability, decisions often become more complex because they are not random. Consider the example of deciding whether to buy a lottery ticket. You have two choices: buy a ticket or do not buy a ticket. The probability of each decision is not .50 (fifty-fifty), however, as you might expect, because many factors help you make that measured decision; your choice is not random. The cost of the ticket, the amount of the jackpot, your feelings about gambling, and even your mood at the moment weigh into your decision. In contrast, the probability of you winning the jackpot if you do decide to buy a ticket is a random event and can be determined by probability. For example, if the back of the ticket states that you have a 1 in 17,000,000 million chance of winning the jackpot, that means that probability of winning the jackpot is .00000005 (calculated by taking 1/17,000,000).
For this week’s Discussion, you will practice your understanding of probability in everyday decision-making by finding an example on the Internet, and you will apply three key features of statistical probability to the example.
To prepare: Search the Internet and review several examples of probability demonstrations.
You may use any simple example of probability that you find (e.g., picking M&Ms or cookies from a jar, spinning a spinner with different colored pieces, the weather). Consider searching the Internet with the phrases “how to teach probability,” or “probability examples,” or “probability demonstrations.” The example you choose can be as elementary as you want as long as it allows you to answer the discussion prompts completely.
Heiman, G. (2015). Behavioral sciences STAT (2nd ed.). Stamford, CT: Cengage.
Chapter 5, “Describing Data with z-Scores and the Normal Curve” (pp. 68–84)
Chapter 6, “Using Probability to Make Decisions about Data” (pp. 88–102)
Chapter 7, “Overview of Statistical Hypothesis Testing: The z-Test” (pp. 106–123)