No matter how well we design our test, perform our calculations and follow our correct procedures, we are still prone to error in our tests. This doesn't necessarily mean we did something wrong in our sampling or our calculations, but just that our calculations gave us an incorrect result. We always have that small random chance of achieving a rare sample that leads us to incorrect results and there are ways that we can minimize this effect.
A Type 1 error is when we reject our Ho, when in fact, we should have failed to reject. This is due to a low p-value that lead us to make a decision, but actually we drew an extremely rare sample from our population. The probability of making a Type 1 Error is the same as our significance level (or 𝞪).
A significance level of 0.05 is usually a good middle ground that minimizes this type of error, but also keeps us from making other errors in our study.
A Type 2 error is when we fail to reject our Ho, but we actually should have rejected our Ho. An easy way to remember this is that we "fail 2 reject in a Type 2 error". This is due to the fact that we did not get a low enough p-value to reject our Ho, but in reality, our Ho is not the truth and there should have been convincing evidence for Ha. Again, 0.05 is a good significance level that minimizes the probability of making this type of error, while also being sure that our calculations obtained are still statistically significant. The probability of making a Type 2 Error is 𝞫. This is easy to remember because the probabilities of a Type 1/2 Error are alpha/beta respectively.
The complement of 𝞫 is known as the power of our test. The power is basically a way of saying how strong our test is because it is the probability of NOT making a Type 2 error. We can increase our power by increasing our sample size. Remember, the larger our sample, the closer our estimate is the population parameter, so the less likely we are to make a mistake.
Common AP Test questions regarding types of errors and power typically ask the following questions:
The first thing AP is likely to ask is how to identify either a Type 1 or 2 error. This is basically writing out the definitions above in context of the given problem. Learn the definitions using the trick above and this part is easy.
Past AP Statistics test also like to ask about the consequence of an error. If we rejected a null hypothesis when we shouldn't have, what are the consequences (in context) of making such an error?
The last thing that AP likes to ask about regarding errors and power is how we can increase power. The answer is always to increase sample size.
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In a recent study, a researcher was testing the claim that 85% of people are satisfied with their current personal reading goals and achievements. The researcher has reason to believe that this proportion is lower and that people are actually not happy with their personal reading plans and need a new library to borrow from. Therefore, the researcher tests the following hypotheses to see if opening a new public library would help people reach their personal reading goals:
a. Describe a Type 2 error in context of the problem. Also, list a consequence of making this type of error.
If the researcher makes a Type 2 error in this problem, he/she has failed to reject the Ho, when in fact it should be rejected. This means that the researcher concluded that we did not have evidence that the true population proportion was less than 0.85, when in fact, there is convincing evidence that it is less than 0.85. A consequence of this error is that people will likely remain largely unhappy with their reading achievement when a new library may help them reach their reading goals.
b. What can the researcher do to increase the power of this test?
The researcher can increase the power of this test and therefore decrease the probability of making a Type 2 error by increasing the sample size in the study.
🎥Watch: AP Stats - Inference: Errors and Powers of Test
✍️ 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
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