1. Reasoning in Psychology Using Statisticsα β
Psychology 138
2017

2. 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

3. 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?

4. 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

5. 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

6. 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

7. 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

8. 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