Exam 1 Study Guide
There will be two types of question formats on the exam, multiple choice and short answer. Study your lecture notes, reading, your homework, and lab notes.
Statistics and Research:
· What is statistics? What are statistics used for?
· What are some common methods used in research?
· What is the difference between quantitative and qualitative research?
· What is the difference between descriptive and inferential statistics?
· What are random sampling and random assignment and why are they important?
Data Collection and Measurement:
· What is the difference between a population and a sample?
· How is a random sample taken and why is it important in statistics?
· What is a variable and what does variable measurement refer to?
· Define independent and dependent variables and identify each in a given study scenario.
· What are the four levels of measurement and what are the differences between them?
· If given a variable, identify its level of measurement and if it is categorical or continuous.
· What are validity and reliability in variable measurement and what is the relationship between them?
· What are ways to improve reliability and validity?
Describing Distributions:
· What information does a frequency distribution show?
· Be able to describe the shapes of distributions of data using the terms in the class readings and lectures.
· Be able to interpret graphed data (pie chart, bar graph/histogram, line graph/frequency polygon).
· What is the difference between a normal distribution, a skewed distribution, and a bimodal distribution?
· What are the important characteristics of a normal distribution (a.k.a. normal curve)?
· What information do the mean, median, and mode of a distribution provide? How are they symbolized?
· Compute measures of central tendency when given a distribution of data.
· Which measure of central tendency is best for the different (a) levels of measurement, and (b) shapes of distributions?
· What information do the range, standard deviation, variance, and sum of squares provide? How are they symbolized?
· What is the difference between a “statistic” and a “parameter?”
· Know the symbols for all statistics and parameters that we’ve discussed so far in this class.
Working with Distributions and Estimating Parameters
· What is sampling? What is sampling with replacement?
· What is the difference between a sample distribution, a population distribution, the sampling distribution of sample means, and the sampling distribution of sample mean differences?
· What is the standard error of the mean and what information does it give? How is it symbolized?
· What is the standard error of the (mean) difference and what information does it give? How is it symbolized?
· What is a z-score? (Hint: what information does it provide?)
· What is an “outlier” and how can outliers be identified in a dataset?
· Identify the (a) percentages and (b) probabilities of scores that fall within 1, 2, and 3 standard deviations from the mean in a normal distribution.
· Compare probabilities of events to determine which one is more or less likely to occur.
· Find the number of standard deviations a score is from the mean of a distribution.
· Find the z-score for a given score in the distribution when you are provided with the mean and standard deviation. Be able to do this for all four types of distributions we studied.
· What is sampling error and why is it important in statistics?
· What does the margin of error tell you?
· Be able to estimate the population mean when given sample data at p < .05 and p < .01. Be able to find the probability of a particular score and to estimate the standard error of the mean when given the margin of error and confidence level (hint: this was discussed in lecture).
Testing Hypotheses:
· What is the purpose of a one sample z-test? What is the purpose of a two sample z-test? Identify when each type is needed.
· What are null and alternative (or research) hypotheses conceptually? Know how to identify and to write null and alternative hypotheses both in words and symbolically.
· Be able to identify directional and nondirectional hypotheses.
· Identify when a one-tailed versus a two-tailed test should be used.
· What is the significance (or alpha) level? (Hint: what does it mean?)
· What is a “critical value” and how is it used in hypothesis testing?
· What is the process for testing the null hypothesis? Know how to conduct a z-test if given data to determine if the null hypothesis can be rejected, and how to state the conclusion in words.
· Know the critical values for one- and two-tailed tests at p < .05 or p < .01.
· What is Type I (alpha) error? What is Type II (beta) error?
· Identify when Type I or Type II error was committed.
· What is the relationship between the significance level and Type I error?
· In statistics, what is power?
· What is the relationship between power and Type II error?
· What are ways to improve power and why do those ways increase power?
Computer Lab: You will not need to use SPSS for the exam but you will need to understand and interpret any SPSS outputs that we have shown you in the computer lab sessions. We recommend that you (a) read the Computer Section of each chapter and (b) study the computer section problems SPSS output and answers that are provided in Canvas for Chapters 3-5.
You will NOT need to calculate: sum of squares (SS), standard error of the mean (s¯(X)), standard error of the difference (sdiff)
You WILL need to memorize: formula for calculating a mean; formula for calculating the (biased) SD or V if given SS; formula to compute variance when given standard deviation and vice-versa; formula for estimating the population mean; formulas for obtaining z-scores for sample, population, sampling distribution of sample means, and sampling distribution of sample mean differences; critical values for z at p < .05 and p < .01