An Introduction to Statistics and Research Design: The Elements of Statistical Reasoning
Aug. 20, 2010
Two Branches of Statistics: Growing Our Knowledge about Human Behavior
Descriptive statistics
Organizing, summarizing, and communicating numerical
infor
Name: _ Date: _ 1. There is a different F distribution for every: A) sample size. B) level of the independent variable. C) combination of sample size and number of samples. D) within-groups degrees of freedom. 2. F is obtained by dividing _ by _. A) SSbet
Discussion section 227 09/14/10 Chapter 5: Z scores and correlation 1. Z score is the distance of a score from its mean in units of standard deviation: how many SD is a score from the mean? 2. Transforming a raw score into a Z score will tells us the raw
Discussion section 227 09/21/10 Regression Regression is a statistical tool that allows us to predict scores of one variable based on the other variable. We are assuming that the relationship between the two variables can best be described by a straight l
Discussion Section 227 10/05/10 Chapter 7: Probability, Z scores, and the Sampling Distribution of the Mean
The normal curve has known mathematical properties and so it is possible to compute the area under the curve and use it to compute probabilities. T
Lab 227 10/12/10 Chapter 8 - Hypothesis Testing and the Z-test What is hypothesis testing? A hypothesis test is a statistical method that uses sample data to evaluate a hypothesis about a population. o We evaluate whether our sample mean is different from
Discussion section 227 10/19/10 One-sample t-test t- tests in general allow us to compare two groups. The one-sample t-test is concerned with comparing one sample to one population and assumes that we don't know the value of the population standard deviat
Discussion section 227 10/26/10 ANOVA (Analysis of Variance) Overview ANOVA is a procedure we use to test the null hypothesis when we want to evaluate the mean differences between two or more groups. The main advantage of ANOVA is that we can make multipl
Discussion section 227 11/09/10 Effect Size, Power, & Confidence Intervals Confidence Intervals Point estimates vs. interval estimates Estimates of population parameters can be either point estimates or interval estimates. A point estimate (e.g. sample me
Discussion section 227 11/16/10 Chapter 12 Chi Square One-variable Chi Square (goodness of fit) This test statistic is used when we have one variable that is measured on a nominal scale. The variable usually consists of several categories We are not compa
Discussion section 227 11/23/10 Chapter 13 Two Variable Chi Square Example 1 A psychologist is interested in finding out about aggressive behavior in men and women under stress. He asked 60 men and 40 women whether they would hit another person of the sam
09/07/10 Discussion Section 227 Chapter 3: Graphs Histogram or frequency polygon: use when our IV is measured on an interval/ratio scale and is continuous; the DV is usually frequency. o The IV goes on X axis while the DV does on the Y axis o The numbers
Discussion Section PSYCH 227 08/31/10 Frequency distributions, central tendency, and variability Frequency Distributions Frequency distribution: a table showing the number of individuals located in each category or score on the scale of measurement. o Wha
Lab 227 08/24/10 Chapter 1 Statistical notation The summation notation is the Greek letter sigma: . Order of operations: 1. Any calculation within parentheses. 2. Squaring (or raising to other exponents) 3. Multiplying or dividing. 4. Summation using the
CHAPTER 4 Probabilities and Research: The Risks and Rewards of Scientific Sampling
Samples and Their Populations: Why Statisticians Are Stingy! Decision making
The risks and rewards of sampling
Risks
1. The sample might not represent the larger population
Chapter 5: Correlation
Correlation: Assessing association between Variables
When trying to find the relationship between two different variables, we usually need to standardize the variables so that they can be compared. To do this, we transform raw score
CHAPTER 6 Regression: Tools for Predicting Behavior
Regression: Building on The difference between regression and correlation Correlation is a statistical tool that enables us Simple Linear regression
to predict an individual's score on the dependent vari
Study guide for exam 1 What you should know: Chapter 1: 1. The differences between descriptive statistics and inferential statistics. 2. The differences between sample and population 3. The differences among nominal variables, ordinal variables, interval
Regression: Tools for Predicting Behavior
Regression: Building on Correlation
The Pearson Correlation Coefficient is a statistic that allows us to quantify the linear relationship between two variables Simple Linear regression is a statistical tool that e
227-1,2,3,4 Malone, Fall, 2010 Review sheet for Chaps 7, 8, 9, and 10 _ Inferential vs descriptive statistics Populations vs samples Parameters vs statistics Greek vs Roman alphabets Properties of the normal curve ubiquitous, simple, well-understood Data
Name: _ Date: _ 1. Increasing sample size: A) decreases the likelihood that we will reject the null hypothesis. B) increases the likelihood that we will reject the null hypothesis. C) has no effect on the likelihood that we will reject the null hypothesis
227-1,2,3,4 Malone, Fall, 2010 Review sheet for Chaps 12, 13, and 15 _ Statistical significance vs practical importance Point estimates Confidence intervals Calculating confidence intervals from z distributions Relation of confidence intervals to null hyp
CHAPTER 2
Descriptive Statistics: Organizing, Summarizing, and Graphing Individual Variables
Organizing our data: A first step in Identifying Patterns
When we organize our data which is composed of raw
scores, or data that not been analyzed, it is useful
Discussion Section 227 11/30/10 Exam 3 Review Selected Topics Point estimates vs. interval estimates Estimates of population parameters can be either point estimates or interval estimates. A point estimate (e.g. sample mean) is precise but likely inaccura
CHAPTER 3 Visual Displays of Data: Graphs That Tell a Story
Uses of Graphs
Positive and negative uses
Can reveal/conceal complicated data Persuade other s to change their attitudes or behaviors Leads others to ask better question
Graphing in the informati
9/27/2010
Point and Interval Estimation
1
Review
Inferential statistics are concerned with forming inferences that extend beyond the immediate sample data to make conclusions about unknown parameters in the larger population Sample statistics are estimat
9/22/2010
Sampling Distributions
1
Overview
Up to this point we have been concerned with describing characteristics of a set of scores (e.g., central tendency, variability, distributional shape) In contrast, inferential statistics concerns the characteri
9/20/2010
The Normal Distribution
1
Probability Distributions
The basis of our inferences from samples to populations are probability distributions
Probability distributions are integral equations that generate curves when they are plotted Think of the
9/13/2010
z-scores
1
Raw Scores
Data are often expressed as raw scores, where subject responses are not transformed from the original data collection
e.g., total number of points earned on a test, number of answers right on a quiz, score on a depression