Introduction
This module introduces students to ways of thinking about and working with data using, as a case study, the analysis of 1.69-oz packages of plain M&Ms. The module is divided into six parts:
Part I. Ways to Describe Data Part II. Ways to Visualize Data Part III. Ways to Summarize Data Part IV. Ways to…

Students will learn to:
Calculate mean and standard deviation from given sample data.
Apply Grubbs test to given sample data for the identification of outliers.
Given an experimental framework, identify and explain the types of calibration methods (curves, addition, and internal standard) used.
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Students will learn to:
Use the appropriate statistical test to evaluate the quality of data.
Use the correct type of t test to make decisions regarding statistical significance.
Compare the average and standard deviation of a data set that of another data set.
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Students will learn to:
understand how a sample relates to a population;
understand how a population can be represented by the normal distribution according to the Central Limit Theorem
understand how a confidence interval can be used to indicate where the true mean is found for a sample.

Students explore basic statistical concepts in this activity.

Students explore basic statistical concepts in this activity.

Students perform statistical analyses of masses of a large number of coins to explore important concepts in statistics.

Students use a dataset for pennies to explore basic statistical concepts.

Students learn to:
Calculate the standard deviation and confidence interval for a data set.
Describe the precision of a data set.
Describe the accuracy of a data set.
Estimate the sensitivity and linear dynamic range from a calibration curve.
Calculate the detection limit and limit of quantitation from a data set.

Students learn to:
Explain the purpose of using internal standards
Use response factors to calculate an unknown concentration based on an internal standard
Determine the limits of detection (LOD) and quantification of a method.