Final Review Fall 2011 Time and Place University Set Psychology 113 Saturday 12 17 2011 12 25 PM 2 25 PM Basics Calculator NO laptop NO cell phones NO other electronic devices Coverage Cumulative Chapter 1 Chapter 10 plus my extra notes on sampling simulation etc Focus on Chapter 7 Chapter 10 Not responsible for all of the sections in the book What is covered in my posted lecture notes and the assigned HW problems Chapter 13 is a way to practice what you learned Closed notes closed book Two formula sheets 8 12 11 sheet both sides Other Questions Practice Problems Look at the HW Problems Look at the HW Solutions gives ideal for getting partial credit Examples covered in lecture notes Examples in your textbook Additional practice problems were emailed with solutions Tables to Know Standard Normal Distribution t Distribution 2 distribution Midterm 1 Material Chapter 1 6 See the posted midterm review Analysis of two samples Chapter 7 and Chapter 9 From the design of the experiment Determine whether you deal with independent samples or paired samples Determine from the text whether a prior knowledge enables you to do a one sided directional test Detect indications that the data do not form a random sample Presence of blocks Using the actual data Perform the independent samples t test and paired samples t test Calculate confidence intervals for the difference in two population means Determine whether a t test is a valid method for a given data set normal probability plots sample sizes The sign test Analysis of two samples In all tests The basic ideas of hypothesis tests Compute a p value Meaning of a p value Type I and II errors Chapter 8 Experimental design Observational versus experimental designs Confounded effects Read a description of the experiment and identify potential problems confounding variable or lack of control group Chapter 10 Analysis of categorical data counts From the text Recognize and formalize the claim Goodness of fit or independence question Using the actual data contingency tables Build contingency tables from the text Perform the chi 2 tests Using the actual data two samples Relative risk odds ratio difference between two proportions Exam Format Exam Format Not like a single HW problem Each part of the question will be like a HW problem Maybe one or two problems will have a stretch part You haven t seen it in HW but we have discussed it in class Potential Questions One question from Chapter 10 categorical One question from Chapter 8 experimental design Verbal description of experiments identify observational vs experimental Identify potential problems with the experiment e g confounding factors etc One combined question Chapters 7 9 10 11 12 I give you a description of data and you tell me what method should be used Probably several parts Examples are in Chapter 13 also in the practice problems There is a lot of material in Chapter 7 And it is related to Chapter 9 It is less obvious how I will ask you this One big question Maybe two smaller questions covering different areas Chapter 10 Question Format The idea is in a single question to test contigency tables difference in proportions odds ratio etc other main ideas from Chapter 10 that we covered Part A confidence interval for difference in proportions interpretation Part B confidence interval for odds ratio interpretation Part C Contigency table Other Concepts Repeated Sampling Interpretation for confidence intervals The Null Distribution of a Hypothesis Test examples t with 14 degrees of freedom 2 with 5 df Null Distribution more details This is the reference distribution we use to compute a p value For example say we observed a test statistic of 1 5 is that too big Well we need to compare it to some reference distribution i e the distribution of the test statistics assuming the null hypothesis is true For simplicity we shorten this to the null distribution or the null distribution of the test statistics Null Distribution continued Two sample problems If we are willing to assume normality of the underlying populations or approximate normality because of the central limit theorem then the null distribution is based on the t distribution hence we refer to it as a t test If we are not willing to assume normality another option is to use a randomization test Here we use a computer to approximate the null distribution There are other tests besides the randomization test when you are not willing to assume normality e g the sign test
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