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1. Introduction to Statistics(0)

Intro to Stats
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Intro to Collecting Data
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Sampling Methods
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2. Describing Data with Tables and Graphs(0)

Visualizing Qualitative vs. Quantitative Data
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Frequency Distributions
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Histograms
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Bar Graphs and Pareto Charts
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Pie Charts
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Frequency Polygons
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Dot Plots
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Stemplots (Stem-and-Leaf Plots)
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Time-Series Graph
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3. Describing Data Numerically(0)

Mean
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Median
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Mode
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Standard Deviation
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Interpreting Standard Deviation
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Percentiles & Quartiles
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Boxplots
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4. Probability(0)

Basic Concepts of Probability
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Complements
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Addition Rule
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Multiplication Rule: Independent Events
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Introduction to Contingency Tables
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Multiplication Rule: Dependent Events
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Bayes' Theorem
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Fundamental Counting Principle
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Counting
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5. Binomial Distribution & Discrete Random Variables(0)

Discrete Random Variables
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Binomial Distribution
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Poisson Distribution
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Hypergeometric Distribution
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6. Normal Distribution & Continuous Random Variables(0)

Uniform Distribution
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Standard Normal Distribution
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Non-Standard Normal Distribution
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7. Sampling Distributions & Confidence Intervals: Mean(0)

Sampling Distribution of the Sample Mean and Central Limit Theorem
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Introduction to Confidence Intervals
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Confidence Intervals for Population Mean
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Determining the Minimum Sample Size Required
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8. Sampling Distributions & Confidence Intervals: Proportion(0)

Sampling Distribution of Sample Proportion
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Confidence Intervals for Population Proportion
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Chi Square Distribution
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Confidence Intervals for Population Variance
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9. Hypothesis Testing for One Sample(0)

Steps in Hypothesis Testing
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Performing Hypothesis Tests: Means
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Performing Hypothesis Tests: Proportions
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Performing Hypothesis Tests: Variance
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Critical Values and Rejection Regions
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Link Between Confidence Intervals and Hypothesis Testing
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Type I & Type II Errors
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10. Hypothesis Testing for Two Samples(0)

Two Proportions
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Two Means - Unknown, Unequal Variance
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Two Means - Unknown, Equal Variance
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Two Means - Known Variance
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Two Means - Matched Pairs (Dependent Samples)
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Two Variances and F Distribution
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11. Correlation(0)

Scatterplots & Intro to Correlation
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Correlation Coefficient
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12. Regression(0)

Linear Regression & Least Squares Method
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Residuals
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Coefficient of Determination
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Inferences for Slope
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Prediction Intervals
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Quadratic Regression
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13. Chi-Square Tests & Goodness of Fit(0)

Goodness of Fit Test
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Contingency Tables
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Independence Tests
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Homogeneity Tests
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14. ANOVA(0)

Introduction to ANOVA
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Multiple Comparisons: Tukey Test
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Multiple Comparisons: Tukey-Kramer Test
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Multiple Comparisons: Bonferoni Test
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Two-Way ANOVA
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9. Hypothesis Testing for One Sample

Steps in Hypothesis Testing

9. Hypothesis Testing for One Sample

Steps in Hypothesis Testing: Videos & Practice Problems

Video Lessons Practice Worksheet

Back to all problems

60 of 0

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Problem 60Multiple Choice

A manufacturer claims that their light bulbs last an average of $1000$ hours. You collect a sample of bulbs and record their lifespans. You can either estimate the average lifespan or test the claim that the mean lifespan is $1000$ hours. What is the main distinction between these two statistical approaches?

Estimation gives a likely range for the mean lifespan, while hypothesis testing evaluates if the data support the claim of a $1000$ -hour mean.

Estimation is only used when the population standard deviation is unknown, while hypothesis testing is used when it is known.

Estimation always uses a $t$ -distribution, while hypothesis testing always uses a $z$ -distribution.

Estimation and hypothesis testing are identical procedures with different names.

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