Reasoning in Psychology Using Statistics α β Psychology 138 2017

Course objectives Mark Twain Scientific reasoning in psychology "There are three kinds of lies: lies, damned lies, and statistics.” Mark Twain Telling the Truth About Damned Lies and Statistics, Joel Best (2001) “Statistics have a bad reputation. We suspect that statistics may be wrong, that people who use statistics may be "lying" -- trying to manipulate us by using numbers to somehow distort the truth. Yet, at the same time, we need statistics; we depend upon them to summarize and clarify the nature of our complex society. This is particularly true when we talk about social problems.” Scientific reasoning in psychology improve your ability conduct and consume psychological research Statistical Literacy the ability to follow and understand arguments from data Course objectives

What are Statistics? “It’s about almost everything in modern society.” Bennett, Briggs, Triola (2003), Statistical Reasoning for Everyday life Statistics are tools, used to make decisions based on data Descriptive statistics Inferential statistics Data are numbers with a context How were the numbers measured, what do they mean? What are Statistics?

Video review Main points from the video Every statistical test starts with an appropriate selection of subjects Inferences must be based on more than one observation because of variability Two types of error must be controlled while testing hypotheses A decision is based on two things: The difference between groups The variability of the scores Inferential statistics: hypothesis testing – rats, robots, and roller skates Wiley (1977) Video review

The research process Scientific Method Ask the research question Identify variables and formulate the hypothesis Define your population Select a research methodology Collect your data from a sample Analyze your data Draw conclusions based on your data Repeat Producing Data Describing Data Conclusions from Data The research process

Methodological basics: what are data and how they are produced Research methods Observation methods Experimental methods Quasi-experimental Variables Types Operational definitions Measurements Continuous and discrete Scales of measurement Sampling Samples and populations Statistics and parameters Techniques Basic Probability Experimental control Reliability Validity Internal and external Confounds & Bias Producing Data

Descriptive Statistics: Statistical tools/procedures to help organize, summarize, and simplify large sets of data (distributions) Describing a single distribution Tables and Graphs Properties Shape, Center, Spread Locating scores & Transforming distributions (z-scores) The Normal distribution (Unit Normal Table) Describing the relationship between 2 distributions Correlation (Pearson’s r) Describing data

Inferential Statistics: Procedures which allow us to make claims about the population based on sample data Distribution of sample means Central Limit Theorem Standard error Error types Type 1 (α) Type 2 (β) Hypothesis testing 1-sample z test 1-sample t test Related samples t-test Independent samples t-test Chi-squared test Correlation and regression Estimation Point estimates Confidence intervals Conclusions from Data