1. Using the default values, click on the "Simulate 1" button. Note the number of successes and failures in each of the two conditions. Note the value of the Chi Square with and without the Yate's correction. Was either significant? Check the lower sectioon that shows the count of the number significant. Most likely, it will show one nonsignificant.
   
   
2. Test whether the Type I error rate is close to the nominal significance level of 0.05. Do this by clicking the "Simulate 5000" button several times. Compare the proportion significant to 0.05. Look at the results both when the Yates correction is never used and when it is used when an expected cell frequency is less than 5. Is the test conservative (proportion significant < 0.05) or is it liberal (proportion significant > 0.05)
   
   
3. Redo the previous simulations when the probability of success is .50 for each condition. Are the results similar? Try making one of the sample sizes 10 and the other one 6.
   
   
4. Try to find a set of parameters such that the proportion significant is greater than 0.06. (Make sure the null hypothesis is always true -- that the probability of success is the same for both conditions.) Could you find such a set of parameters? Are there many circumstances in which the test is that liberal? Do you think the Yates correction is a good procedure?
   
   
5. Now consider cases in which the probability of success is different for the two conditions. Here the null hypothesis is false so the higher the proportion significant the better. What is the effect of using the Yates correction on rejecting a false null hypothesis?