In chapter 15, we learn about cause and effect. Cause and effect is A then B or A caused B. One thing in chapter 15 is the cause and effect in populations. I found that really interesting. Cause in populations is “usually explained as meaning that given the cause, there’s a higher probability that the effect will follow than if there were not the cause” (EPSTEIN). So basically, one can tell that the claim is a cause and effect if there is a probability. For example, people who drink alcohol and drive have a higher probability in getting a DUI ticket than the people who does not drink. If there is a probability, then an effect will have to follow because that is how cause and effect goes. It comes together like peanut butter and jelly, or the prince and princess. Also, in cause and effect in populations, there are three sections below it. It is controlled experiment: cause to effect, uncontrolled experiment: cause to effect, and uncontrolled experiment: effect to cause.
I like how you made the chapter summarized in such a simple reader’s digest, since I thought the chapter was longer than the average amount of reading we had to read from Epstein’s text. I like how you also mentioned about how cause and effect goes together like pairs such as peanut butter and jelly or the prince and the princess. Your example of drinking and driving causing a probability of getting a DUI is a nice example of cause and effect because there is a chance of getting a DUI from drinking and driving. Drinking and driving is a cause to many effects, such as accidents that can result to major injuries or perhaps death.
ReplyDelete