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How do statistics apply to your life and how can we use statistics to draw conclusions about the world? This course will provide you with an integrated and engaging online experience to explore statistics in the behavioral sciences.
Can you think of an area of your life that is influenced by statistics? Many times when we think about statistics in our daily lives, we think about numerical expressions of statistics, such as the number of daily COVID cases in our county, the percentage of students admitted each year to our university, or the number of people that voted in the last election. From each of these examples, we could go on to make inferences or look to answer questions based on this data, such as whether to open restaurants, how many new students are psychology majors, or if a specific issue drove voters to the polls in a specific state.
This course will begin by introducing the basic concepts of how to describe and visualize data, the fundamentals of using statistics to make inferences, and the logic of null hypothesis testing. Various types of hypothesis tests will be introduced, along with criteria for selecting which is appropriate for different study conditions. As an extension of null hypothesis significance tests, you will learn about how to interpret effect sizes and confidence intervals, along with statistical power, before being introduced to alternatives to null hypothesis significance testing. All this is fleshed out in Data Analysis for the Behavioral Sciences.
High school reading level
Exposure to university-level courses or course materials
Explain various ways to categorize variables.
Explain various ways to describe data.
Describe how graphs are used to visualize data.
Explain the meaning of a correlation coefficient.
Describe the logic of inferential statistics.
Explain the logic of null hypothesis significance testing.
Select the appropriate inferential test based on study criteria.
Compare and contrast the use of statistical significance, effect size, and confidence intervals.
Explain the importance of statistical power.
Describe how alternative procedures address the major objections to null hypothesis significance testing.
Data Analysis for the Behavioral Sciences
Learning Plan
Data Analysis Basics
Variables and Measures
Describing Data
Section Summary
Null Hypothesis Significance Testing
Inferential Statistics
Null Hypothesis Significance Testing
The Variety of Null Hypothesis Significance Tests
Beyond Null Hypothesis Significance Testing
Preview
The “New Statistics”
Statistical Power
Alternatives to Null Hypothesis Significance Testing
Course Summary
Explain various ways to categorize variables
Explain various ways to describe data
Explain the meaning of a correlation coefficient
Describe the logic of inferential statistics
Explain the logic of null hypothesis significance testing
Select the appropriate inferential test based on study criteria
Compare and contrast the use of statistical significance, effect size, and confidence intervals
Explain the importance of statistical power
