Psychology Experiments For Students

Correlation Does Not Equal Causation: Scientific Method and Critical Thinking Lesson for Middle Grades or High School
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Scientific Method and Critical Thinking Lesson for Middle or High School Students This lesson walks students through correlation and true experiments in an easy-to-understand and engaging manner. 12 different correlation examples are included. The package includes handouts, notes, diagrams, practice questions, a review, and answer key. How to Use : Print a 7 page package for each of your students. Present pages 1-4 on an interactive whiteboard and teach your students the basics of correlation (scatterplots, positive correlation, negative correlation, correlation coefficients, mistaking correlation as causation, media examples, reverse causation, third variables, etc.) as they fill in questions and notes. Then, students can do the practice questions on their own or in small groups (answer key included). Next, present page 6 to explain how to actually find causation in a true experiment: random selection, random assignment, dependent variable, independent variable, experimental group, control group, etc. Finally, students complete a one-page review (answer key included) Grades to Use With: This lesson was designed to be accessible to students as young as grade six all the way through high school. It could be an addition to a science unit about experimental methods, a science fair unit, a grade 8 math unit about scatter plots, or even a psychology class. Standards : CCSS8.SP.A.1 Construct and interpret scatter plots for bivariate measurement data to investigate patterns of association between two quantities. Describe patterns such as clustering, outliers, positive or negative association, linear association, and nonlinear association. CCSS8.SP.A.2 Know that straight lines are widely used to model relationships between two quantitative variables. For scatter plots that suggest a linear association, informally fit a straight line, and informally assess the model fit by judging the closeness of the data points to the line.