Description Recap So far, we have learned about graphing, scientific notation, measures of central tendency, and measures of spread. There is one more important concept underlying statistics - probability! In behavioral statistics we typically apply probability to whole populations, trying to understand how individuals or samples compare to a population. For example, what is the chance that a person will be significantly taller than the rest of a population if they have a certain gene. Or, what is the chance that a certain ethnic group might have more risk for getting cancer. But to start with, we are going to brush up on our basic understanding of probability. It will definately help you to be able to easily convert between decimals and fractions and probability as well. 1) To get an idea of how probabilty works, | want you to watch this video. Write down some of the examples in the video as you watch, and periodically pause to try the solutions for yourself as you watch. Watch on (B8 YouTube 2) Read the following text (its short) to solidify some of the ideas from the video Kami Export - f55Dy_PLGIdVp8R5qglK2A.pdf 3) Your assignment over and over. The applet does not have fancy graphics but I think it gets the point across. As you work, write/draw out your observations on a sheet of paper, then upload a pic of your work. In this applett scenario, you are drawing candies from a gumball machine filled with Reece's Pieces. In the top left corner of the applet you can change the probability of getting a certain outcome, You can change how many candies you take out of the machine, and you can change the number of times you sample from the population. At default, it tells you there is a probability of p=0.5 of pulling an orange candy. Itisset to pull out 25 candies at a time, and it will take out one sample of 25. To start the simulation click "draw samples." After you click "draw sample" it will calculate the total number of orange candies out of 25, and plot that on the histogram. a) want you to start with the default settings. \\Write: If the probability of pulling an orange candy is 0.5, and you pull 25 candies, how many orange candies would you expect to pull? b) Try the default settings about 5 times (5 samples) to see how what the distribution looks like after 5 samples - Draw out what you see on a piece of paper. \\Write: was this what you expected and why or why not? c) Now increase the sample to 10 samples (you can type "10" into the "number of samples\" box). Draw out what you see now. d) Increase the sample size to 100 samples. (Note: If you unclick "Show Animation" it will run faster) Draw out what you see. e) Finally, increase the sample size to 1000 samples. Draw out what you see. Write your ideas: 1) how did the shape of your histogram change the more times you took a sample, 2) compare your expected result from answer 1 with the overall mean of the population after you took 1000 samples, how do they compare? f) Try the same steps again, but take a sample size of 3 candies, for a total of 1000 samples, probability of p=0.5. Looking at your final histogram , how would you compare your results to having a sample size of 25 candies? g) Try the same steps again, but take a sample size of 100 candies, for a total of 1000 samples, probability of p=0.5. Looking at your final histogram in this case, how would you compare your results to having a sample size of 25 candies, or a sample size of 3? Write down your thoughts: How does sample size affect the shape of a histogram