MAFS.912.S-ID.3.9Archived Standard

Students are asked to interpret a correlation coefficient in context and describe a possible causal relationship.

Type: Formative Assessment

Students are asked to identify all possible causal relationships between two correlated variables.

Type: Formative Assessment

Students are given a statement of association between two variables and are asked to determine if one variable is a cause of the other.

Type: Formative Assessment

Students are given a scenario describing an association between two variables and are asked to determine if one variable is a cause of the other.

Type: Formative Assessment

The changing climate is an important topic for both scientific analysis and worldly knowledge. This lesson uses data collected by the National Snow and Ice Data Center to create and use mathematical models as a predictive tool and do critical analysis of sea ice loss.

Type: Lesson Plan

This lesson introduces the students to the concepts of correlation and causation, and the difference between the two. The main learning objective is to encourage students to think critically about various possible explanations for a correlation, and to evaluate their plausibility, rather than passively taking presented information on faith. To give students the right tools for such analysis, the lesson covers most common reasons behind a correlation, and different possible types of causation.

Type: Lesson Plan

In this lesson students will determine whether their is a relationship between two variables through a Perspectives Video and real-life examples. Students will learn about confounding variables and investigate why correlation does not imply causation.

Type: Lesson Plan

This resource provides a single 50-minute period introductory lesson on Correlation, the Correlation Coefficient, and Correlation vs. Causation. The lesson is structured around collecting data from a survey at the beginning of class to be used in creating scatter plots and analyzing them using technology. Students engage in discussion activities that challenge their thoughts on linked variables in the media.

Type: Lesson Plan

In this lesson, Algebra 1 students will use supplied heart rate data to determine if heart rate and the amount of time spent exercising each week are correlated. Students will create their own scatter plots and lines of best fit for the data and study correlation using GeoGebra. Students will gather evidence to support or refute statistical statements made about correlation. The lesson provides easy to follow steps for using GeoGebra, a free online application, to generate a correlation coefficient for two given variables.

Type: Lesson Plan

Students investigate correlation and causation through the medium of cartoons. Students construct arguments in favor of and against causal relationships between two strongly correlated events and decide which one is more reasonable. Students create cartoons representing the idea that correlation does not imply causation.

Type: Lesson Plan

Students will learn how to analyze whether two events/properties demonstrate a correlation or causation or both. They will learn what factors are involved when evaluating whether or not correlated events demonstrate causation. If two events are claimed to be causal when they are not, they will be able to determine why and which (if any) causal fallacies are present. At the close of the lesson students will be given situational data and develop a newscast that assumes causation when in fact there is no causal link. Students who are observing will analyze each presentation and determine which (if any) causal fallacy was used (or explain why the newscast is correct in their assumption of causality).

Type: Lesson Plan

Using Cornell Notes and a PowerPoint Presentation, students will learn to distinguish between correlation and causation. They will build their skills by playing two interactive digital games that are included in the lesson. The lesson culminates with a research project that requires students to find and explain the correlation between two real world events.

Type: Lesson Plan

Students will explore correlation and causation from data through class discussions of real world examples. They will know positive, negative, strong, and weak correlation. Students make predictions regarding feasibility of causation by analyzing graphs and scatter plots of data.

Students will participate in an experiment where they will generate and analyze their own data. They will come to conclusion regarding variations in data, correlation and causation. Students are encouraged to explain and justify their responses. Teacher will facilitate discussion of leading question to be geared towards the learning objectives.

During the lesson, students will be assessed by several formative assessments and a summative assessment at the conclusion. The lesson includes the a worksheet and data collection sheets to be concluded.

Type: Lesson Plan

Jens Foell discusses the link between correlation and causation in PTSD patients.

Download the CPALMS Perspectives video student note taking guide.

Type: Perspectives Video: Expert

Florida State Researcher, Jens Foell, discusses the importance of understanding correlation versus causation when researching personality traits and criminal behavior.

Download the CPALMS Perspectives video student note taking guide.

Type: Perspectives Video: Expert

Watching this video will cause your critical thinking skills to improve. You might also have a great day, but that's just correlation.

Download the CPALMS Perspectives video student note taking guide.

Type: Perspectives Video: Professional/Enthusiast

This is a simple task addressing the distinction between correlation and causation. Students are given information indicating a correlation between two variables, and are asked to reason out whether or not a causation can be inferred.

Type: Problem-Solving Task

This problem solving task asks students to examine the relationship between shops and crimes by using a correlation coefficient.

Type: Problem-Solving Task

This informational text resource is intended to support reading in the content area. Many people are confused about the concept of correlation versus causation. To help demonstrate the misconception in a light and humorous way, this article describes the work of Tyler Vigen. The Harvard student graphs data that are highly correlated but clearly unrelated. The "spurious correlations" help debunk the myth that if there is a correlation, then there is a causal relationship. The article emphasizes that rational human thought is essential to process the relationships and is necessary for studying statistics.

Type: Text Resource

Type: Unit/Lesson Sequence