Public learning track
Correlation vs. Causation, Media Literacy, and Inferential Reasoning
10th Grade · Math · Open Global Math
7 lessons
Goal
Critique statistical claims in media, differentiate correlation from causation, and evaluate sampling designs and inferential validity.
Featured Diagrams
Evaluating Causation Claims from Data Analysis
7 lessons
0 of 7 done
- 1Evaluating Causation Claims from Data AnalysisUp nextStart
- 2Observational Studies vs. Randomized Controlled ExperimentsPremiumNot started yet
- 3Sampling Methods: Simple Random, Stratified, Cluster, and Voluntary BiasNot started yet
- 4Critiquing Biased Samples and Exaggerated Claims in the MediaNot started yet
- 5Sample-to-Population Inferences and Margin of ErrorNot started yet
- 6Common Statistical Fallacies: Simpson's Paradox and Gambler's FallacyNot started yet
- 7Synthesizing a Statistical Investigation and Writing Justified ConclusionsNot started yet
Standards Covered
- OGM.11.STA.4Open Global Math Standards 2026™ · Math · Level 11 / High School 2 (Age 15) · Grade 10Analyze bivariate quantitative and categorical data using scatter plots and two-way tables; fit linear regression models (lines of best fit); evaluate correlation versus causation; and critique statistical claims and media reports using sample-to-population inferential reasoning.
- OGM.11.STA.1Open Global Math Standards 2026™ · Math · Level 11 / High School 2 (Age 15) · Grade 10Calculate probabilities of single, combined, mutually exclusive, and independent events using Venn diagrams, tree diagrams, two-way tables, and sample spaces; apply the addition rule, product counting rule, permutations, combinations, and conditional probability formula; understand the Law of Large Numbers (Laplace rule) and risk analysis.
- OGM.11.STA.2Open Global Math Standards 2026™ · Math · Level 11 / High School 2 (Age 15) · Grade 10Calculate and interpret measures of center (mean, median, mode) and dispersion (range, interquartile range, variance, standard deviation) for population and sample parameters (μ, σ, x̄, s); construct and interpret histograms (equal/unequal class width, frequency density), cumulative frequency graphs, box-and-whisker plots, and dot plots.
Curriculum Framework
10th Grade · Math · Open Global Math
1
8 lessons
Univariate Data: Visual Displays, Frequency Distributions, and Ogives
Construct and interpret visual representations of quantitative data including histograms, dot plots, box plots, and cumulative frequency curves.
2
7 lessons
Univariate Data: Measures of Center, Dispersion, and Linear Transformations
Calculate and interpret central tendency and spread parameters for populations and samples, analyzing the effects of linear data transformations.
3
7 lessons
Combinatorics, Sample Spaces, and Theoretical Probability
Apply the fundamental counting principle, permutations, combinations, and sample space models to compute exact probabilities.
4
7 lessons
Compound Probability, Conditional Events, and Risk Analysis
Solve multi-event probability problems using Venn diagrams, tree diagrams, two-way tables, conditional formulas, and risk metrics.
5
9 lessons
Discrete Random Variables and Binomial Probability Models
Define discrete probability mass functions, compute expected values and variances, and model Bernoulli/Binomial processes.
6
8 lessons
Continuous Random Variables and the Normal Distribution
Analyze the normal distribution curve, compute z-scores, and calculate probabilities and percentiles using the standard normal model.
7
7 lessons
Bivariate Quantitative Data: Scatter Plots and Linear Regression
Analyze bivariate relationships, fit least-squares regression lines, interpret correlation coefficients, and examine residuals.
8
7 lessons
Correlation vs. Causation, Media Literacy, and Inferential Reasoning
Critique statistical claims in media, differentiate correlation from causation, and evaluate sampling designs and inferential validity.