Critical Thinking: Analyze data to determine the significance of differences between two sample groups.
Communication: Articulate the process and results of hypothesis testing clearly and effectively.
Learner’s Mindset: Seek to understand the rationale behind using hypothesis tests for comparing samples.
What are the steps involved in conducting a hypothesis test with two samples?
How do we determine whether the difference between two sample means is statistically significant?
Why is it important to compare two sample groups in statistical analysis?
Formulate null and alternative hypotheses for comparing two sample groups.
Conduct hypothesis tests using t-tests or z-tests for two samples.
Interpret the results of hypothesis tests and understand their implications.
Classify Hypothesis Tests by Type:
Understanding different types of hypothesis tests (e.g., z-tests, t-tests, tests for proportions).
Conduct and Interpret Hypothesis Tests:
For two population means (population standard deviations known).
For two population means (population standard deviations unknown).
For two population proportions.
For matched or paired samples.
CCSS.MATH.CONTENT.HSS.IC.B.5: Use data from a randomized experiment to compare two treatments; use simulations to decide if differences between parameters are significant.
CCSS.MATH.CONTENT.HSS.IC.B.6: Evaluate reports based on data.
Textbooks:
"The Practice of Statistics" by Daren S. Starnes and Josh Tabor.
"Statistics for Business and Economics" by Paul Newbold, William L. Carlson, and Betty Thorne.
Online Tutorials:
Khan Academy: Hypothesis Testing
Coursera: Introduction to Statistics
Software Tools:
Excel for conducting hypothesis tests.
R or Python for more advanced statistical analysis.
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