✍️ Free Response Questions (FRQs)
👆 Unit 1 - Exploring One-Variable Data
1.4Representing a Categorical Variable with Graphs
1.5Representing a Quantitative Variable with Graphs
1.6Describing the Distribution of a Quantitative Variable
1.7Summary Statistics for a Quantitative Variable
1.8Graphical Representations of Summary Statistics
1.9Comparing Distributions of a Quantitative Variable
✌️ Unit 2 - Exploring Two-Variable Data
2.0 Unit 2 Overview: Exploring Two-Variable Data
2.1Introducing Statistics: Are Variables Related?
2.2Representing Two Categorical Variables
2.3Statistics for Two Categorical Variables
2.4Representing the Relationship Between Two Quantitative Variables
2.8Least Squares Regression
🔎 Unit 3 - Collecting Data
3.5Introduction to Experimental Design
🎲 Unit 4 - Probability, Random Variables, and Probability Distributions
4.1Introducing Statistics: Random and Non-Random Patterns?
4.7Introduction to Random Variables and Probability Distributions
4.8Mean and Standard Deviation of Random Variables
4.9Combining Random Variables
4.11Parameters for a Binomial Distribution
📊 Unit 5 - Sampling Distributions
5.0Unit 5 Overview: Sampling Distributions
5.1Introducing Statistics: Why Is My Sample Not Like Yours?
5.4Biased and Unbiased Point Estimates
5.6Sampling Distributions for Differences in Sample Proportions
⚖️ Unit 6 - Inference for Categorical Data: Proportions
6.0Unit 6 Overview: Inference for Categorical Data: Proportions
6.1Introducing Statistics: Why Be Normal?
6.2Constructing a Confidence Interval for a Population Proportion
6.3Justifying a Claim Based on a Confidence Interval for a Population Proportion
6.4Setting Up a Test for a Population Proportion
6.6Concluding a Test for a Population Proportion
6.7Potential Errors When Performing Tests
6.8Confidence Intervals for the Difference of Two Proportions
6.9Justifying a Claim Based on a Confidence Interval for a Difference of Population Proportions
6.10Setting Up a Test for the Difference of Two Population Proportions
😼 Unit 7 - Inference for Qualitative Data: Means
7.1Introducing Statistics: Should I Worry About Error?
7.2Constructing a Confidence Interval for a Population Mean
7.3Justifying a Claim About a Population Mean Based on a Confidence Interval
7.4Setting Up a Test for a Population Mean
7.5Carrying Out a Test for a Population Mean
7.6Confidence Intervals for the Difference of Two Means
7.7Justifying a Claim About the Difference of Two Means Based on a Confidence Interval
7.8Setting Up a Test for the Difference of Two Population Means
7.9Carrying Out a Test for the Difference of Two Population Means
✳️ Unit 8 Inference for Categorical Data: Chi-Square
📈 Unit 9 - Inference for Quantitative Data: Slopes
🧐 Multiple Choice Questions (MCQs)
Is AP Statistics Hard? Is AP Statistics Worth Taking?
Best Quizlet Decks for AP Statistics
⏱️ 2 min read
June 8, 2020
To avoid the effects of bias on your data, the best method of implementing a study is by using random sampling in which a chance process is used to determine which members of a population are included in the sample.
Simple Random Sample (SRS) chooses a sample size “n” in a way that a group of individuals in the population has an equal chance to be selected as the sample.
Steps to choose an SRS using a TI-84 Calculator:
Label each individual in the population with a different label from 1 to “N,” where N is the total number of individuals in the population.
Randomize the way you choose the individuals for the sample. Use a random number generator to get “n” different integers from 1 to N, where n is the sample size.
Select the individuals that were chosen by the calculator.
*It is important to indicate when you conduct an SRS without replacement in your answer since selections are often made with replacement. In simpler terms, without replacement means once an individual is chosen for a sample, the number or label cannot be chosen again. 📚Strata are groups of individuals in a population who share characteristics thought to be associated with the variables being measured in a study. Stratified Random Sample selects a sample by choosing an SRS from each stratum and combining the SRSs into one overall sample. This method works best when the individuals within each stratum are similar depending on the variable being measured and when there are large differences between each stratum. *Stratified random samples reduce variability in the data and give more precise results.
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A cluster is a group of individuals within the population that are located near each other. Cluster Sampling selects a sample by randomly choosing clusters including each member of the selected clusters in the sample. This method is resourceful when the clusters look like the population but one a smaller scale. It saves time and money.
*For cluster sampling, you want each cluster to represent a portion of the population.
*For all methods, use chance to determine labels and groups to eliminate bias.
Being able to identify which method to use depends on what the question is asking. Identify the population and variables to figure out how large of a difference there is between the sample. Make your decision based on that information.
🎥Watch: AP Stats - Sampling Methods and Sources of Bias
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