Table of contents
- 1. Intro to Stats and Collecting Data55m
- 2. Describing Data with Tables and Graphs1h 55m
- 3. Describing Data Numerically1h 45m
- 4. Probability2h 16m
- 5. Binomial Distribution & Discrete Random Variables2h 33m
- 6. Normal Distribution and Continuous Random Variables1h 38m
- 7. Sampling Distributions & Confidence Intervals: Mean1h 3m
- 8. Sampling Distributions & Confidence Intervals: Proportion1h 12m
- 9. Hypothesis Testing for One Sample1h 1m
- 10. Hypothesis Testing for Two Samples2h 8m
- 11. Correlation48m
- 12. Regression1h 4m
- 13. Chi-Square Tests & Goodness of Fit1h 20m
- 14. ANOVA1h 0m
4. Probability
Basic Concepts of Probability
Problem 4.1.35
Textbook Question
In Exercises 33–40, use the given probability value to determine whether the sample results are significant.
Voting Repeat Exercise 33 after replacing 40 Democrats being placed on the first line of voting ballots with 14 Democrats being placed on the first line. The probability of getting a result as low as 14 is 0.029792.

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Step 1: Understand the problem. The goal is to determine whether the sample result of 14 Democrats being placed on the first line of voting ballots is statistically significant, given the probability of obtaining such a result is 0.029792.
Step 2: Recall the concept of statistical significance. A result is typically considered statistically significant if the probability (p-value) of obtaining it is less than a commonly used threshold, such as 0.05 (5%).
Step 3: Compare the given probability (0.029792) to the significance threshold (0.05). If the probability is less than 0.05, the result is statistically significant; otherwise, it is not.
Step 4: Interpret the comparison. If the probability is less than 0.05, it suggests that the observed result (14 Democrats on the first line) is unlikely to have occurred by random chance alone, indicating statistical significance.
Step 5: Conclude whether the sample result is significant based on the comparison. Ensure that the reasoning aligns with the threshold and the given probability value.

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Key Concepts
Here are the essential concepts you must grasp in order to answer the question correctly.
Significance Level
The significance level, often denoted as alpha (α), is a threshold used in hypothesis testing to determine whether to reject the null hypothesis. Commonly set at 0.05, it represents the probability of making a Type I error, which occurs when the null hypothesis is incorrectly rejected. In this context, if the probability value (p-value) is less than α, the results are considered statistically significant.
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Step 4: State Conclusion Example 4
P-value
The p-value is a statistical measure that helps determine the strength of the evidence against the null hypothesis. It quantifies the probability of observing results as extreme as, or more extreme than, the actual observed results, assuming the null hypothesis is true. A low p-value (typically less than 0.05) indicates strong evidence against the null hypothesis, suggesting that the observed effect is unlikely to have occurred by random chance.
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Step 3: Get P-Value
Hypothesis Testing
Hypothesis testing is a statistical method used to make inferences about population parameters based on sample data. It involves formulating a null hypothesis (H0) and an alternative hypothesis (H1), then using sample data to calculate a test statistic and corresponding p-value. The results help determine whether to reject the null hypothesis in favor of the alternative, based on the significance level and the p-value.
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Step 1: Write Hypotheses
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