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* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
You can use the following resources in the exam: One page of notes (you can use both sides) A calculator A table of areas under the standard normal distribution and a table of critical t-scores, and F ratios (We will provide these for you) We will give you scrap paper for doing computations RESEARCH DESIGN You should understand the following concepts and be able to identify them given a hypothetical research project and to apply them to the interpretation of an experiment’s results: Experiments vs. observational studies (and their pros and cons) Independent vs. dependent variables Between subject vs. within subject designs (and their pros and cons) What it means to counterbalance the order of a within-subject manipulation You should understand the importance of random, representative sampling in research and of random assignment to experimental conditions in experiments. DESCRIPTIVE STATISTICS Given a hypothetical set of data you should be able to identify which method of visualization is most appropriate (bar graph, histogram, polygon, or scatter plot), which measure of central tendency is most appropriate (mean, median, or mode), and which measure of dispersion is most appropriate (standard deviation, interquartile range, or entropy). You should also be able to explain why your chosen type of graph or descriptive statistic is the most appropriate. You should be able to identify data as: Nominal Ordinal Interval/Ratio You should be able to label data distributions as: Normal Positively Skewed Negatively Skewed Bimodal Supergaussian Subgaussian You need to know how to read the following graphs: Histogram Bar graph Frequency polygon Boxplot Scatterplot Note: You will NOT be asked to produce any graphs. But you need to be able to interpret them, to know when a particular type of graph is an appropriate way to visualize the data, and to identify misleading graphs. From a scatter plot, you should be able to determine if linear or rank correlation is a better way of quantifying the relationship between two variables. You should also be able to guess the linear correlation coefficient Pearson’s r from a scatter plot. Given Pearson’s r for a data set, you should be able to interpret it (e.g., if two variables are correlated with an r of .56, what does that mean?). You will NOT need to know how to compute the mode, interquartile range, range, or entropy. You will NOT need to compute frequency distributions. PROBABILITY & INFERENTIAL STATISTICS Given a hypothetical data set and research question, you should be able to identify which hypothesis test is most appropriate to analyze the data. The possible options I expect you to know are: z-test one sample t-test binomial/sign test repeated measures t-test independent samples t-test Mann-Whitney U test t-test of Pearson’s linear correlation coefficient r rank correlation (e.g., Kendall’s tau or Spearman’s rho) independent samples one factor ANOVA repeated measures one factor ANOVA independent samples two-factor ANOVA linear regression “none of these” You should know how to compute a z-test, one sample t-test, and a repeated measures t-test (this includes deriving p-values, confidence intervals, and Cohen’s d). You should be able to fill out hypothesis testing forms for JUST these 3 tests. You should be able to complete an ANOVA table given all the sums of squares (i.e., you will have to figure out the degrees of freedom, mean squares, F ratios, p-values of F ratios, and determine if an F ratio is significant at a given alpha level—see Question 11 on the practice final for example). You should be able to compute the eta squared (η2) and partial eta squared (ηp2) for ANOVA effects and interpret Tukey’s HSD to find out which pairs of cells differ. You will NOT have to perform Tukey’s HSD. You should be able to read two factor ANOVA plots to determine which main effects appear to be significant and if the interaction appears to be significant (see HW 9 Problem 9.D on WebCT for an example). You should be able to read the results of a hypothesis test and to think critically about them (e.g., What do the results suggest about the likelihood that the null or alternative hypothesis is true? What do the results suggest about the size of any potential effect?) You should understand and be able to explain the following concepts: Population distribution vs. the sampling distribution of the mean (Chapters 8-9) The central limit theorem What it means to reject or retain the null hypothesis Type I and Type II error and ways to reduce/increase the probability of making them The alpha level of a test The p-value of a test statistic Cohen’s d The power of a hypothesis test The effect of sample size on hypothesis tests and estimation The difference between parametric (e.g., t-tests) and nonparametric (e.g., the MannWhitney U test) tests. Why multiple comparisons are problematic The purpose of following up ANOVAs with a multiple comparisons test like Tukey’s HSD eta squared (η2) and partial eta squared (ηp2) The concept of “main effects” and “interactions” in a two factor ANOVA The purpose of following up two factor ANOVAs with a “simple effects test” You do NOT need to know how to compute: an independent samples t-test a binomial/sign test Mann-Whitney U test Wilcoxon T Power curves correlation coefficients linear regression sums of squares for an ANOVA simple effects tests the formula for binomial distributions Basic probability problems like the first questions 4,5, and 6 on practice midterm II Conditional Probability Bayes’s rule Tukey’s HSD Again, you do NOT need to know how to fill out hypothesis testing forms (except for the 3 tests I expect you to be able to compute: z-test, one sample t-test, repeated measures t-test.