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This resource was created for Introduction to Statistics students at the University of Maryland, and is designed to help you explore psychological theory, research, and practical applications of statistics. After completing this course in psychology, you will be able to:
Explain how to use and interpret descriptive and inferential statistics in an ethically responsible way.
Describe the difference between descriptive (central tendency, dispersion, correlation) and inferential statistics (single, multiple, logistic), and know when to use each.
Demonstrate analytical skills by critiquing research and media claims.
Apply statistical concepts and methods in a way that improves your own academic, personal, and professional life.
Each module is structured around key prompts - Learning Objective Questions - and followed by the links to articles, videos, and interactive demonstrations you will need to answer those questions. After studying the readings, videos, and presentations you should be able to answer the learning objective questions in detail without any notes in front of you. If you practice doing that regularly, you are well prepared for any assessment that your instructor can give you!
What are statistics? Why should I care about them? How should I approach learning to succeed in this (and all of my other) course(s)? What do careers in statistics look like?
What makes psychology a science? It's all in the methods!
See what you're working with! A picture may be worth a thousand words, but it can be worth an infinite amount of numbers.
The spice of life is variety! The spread of data is variability!
Standardization allows for meaningful comparisons that will not put you to zzzzzz!
Is all about those (linear) relationships!
Is all about predicting those (linear) relationships!
You will probably love this section!
What's "normal" anyways?
Let's get testing!
Let's test success! Do we have more successes than failures?
We get samples from populations and statistics from samples and sampling distributions from statistics!
Is your continuous sample statistics significant?
With great statistical power, comes great responsibility.
Wait, NHST isn't perfect?
I want to use a z-test but I can't! What do I do? I do a one-sample t-test!
Where is that population mean?!
Are these two groups of people really (significantly) different?
Do people change (due to a research manipulation)?
But wait, what do I do if I have more than two groups?! Analysis of Variance (ANOVA) to the rescue!
But, real life has more than one variable! I want to study more than one causal variable! N-way ANOVA to the rescue!
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