Sure, here are some flashcards based on important information from Chapter 9:

1. Hypothesis Testing:

   - Definition: A statistical method used to make inferences about population parameters based on sample data.

2. Null Hypothesis (H0):

   - Definition: The hypothesis that there is no significant difference or relationship between variables in the population.

   - Denoted as H0.

3. Alternative Hypothesis (H1 or Ha):

   - Definition: The hypothesis that states there is a significant difference or relationship between variables in the population.

   - Denoted as H1 or Ha.

4. Type I Error:

   - Definition: Incorrectly rejecting the null hypothesis when it is true.

   - Also known as a false positive.

   - Controlled by the significance level (α).

5. Type II Error:

   - Definition: Failing to reject the null hypothesis when it is false.

   - Also known as a false negative.

   - Controlled by the power of the test (1 - β).

6. Significance Level (α):

   - Definition: The probability of making a Type I error.

   - Commonly set at 0.05 or 0.01.

7. P-value:

   - Definition: The probability of observing a test statistic as extreme as the one calculated, assuming the null hypothesis is true.

   - If p-value < α, reject H0.

8. Critical Value:

   - Definition: The value beyond which the null hypothesis is rejected.

   - Determined based on the chosen significance level and the distribution of the test statistic.

9. One-tailed Test:

   - Definition: A hypothesis test in which the alternative hypothesis is directional, focusing on one side of the distribution.

   - Used when there is a specific directional expectation for the effect.

10. Two-tailed Test:

    - Definition: A hypothesis test in which the alternative hypothesis is non-directional, focusing on both sides of the distribution.

    - Used when there is no specific directional expectation for the effect.

Certainly! Here are some flashcards based on important information from Chapter 10:

1. Confidence Intervals:

   - Definition: Intervals that provide a range of values for a population parameter estimate, reflecting the uncertainty associated with the estimate.

   - Widely used in statistics to quantify uncertainty.

2. Point Estimate:

   - Definition: A single value used to estimate a population parameter based on sample data.

   - Example: Mean weight of adults estimated from a sample.

3. Standard Error:

   - Definition: A measure of the variability of a sample statistic.

   - Depends on the population standard deviation and sample size.

4. Interpretation of Confidence Intervals:

   - They provide a range of values consistent with the sample data, not a probability distribution for the parameter itself.

   - A 95% confidence interval means that in repeated sampling, 95% of the intervals will contain the true population parameter.

5. Computing Confidence Intervals:

   - Normal Distribution: When population standard deviation is known.

   - T Distribution: When population standard deviation is unknown and sample size is small.

   - Bootstrap: When the distribution of the statistic is unknown or non-normal.

6. Effect Size:

   - Definition: A standardized measure that quantifies the size of a statistical effect.

   - Helps to understand the practical significance of results.

   - Examples: Cohen's d, Pearson's r, Odds ratio.

7. Cohen's d:

   - A common measure of effect size used for comparing means.

   - Interpretation: Small (0.2), Medium (0.5), Large (0.8).

8. Pearson's r:

   - Correlation coefficient used to measure the strength of linear relationships between continuous variables.

   - Ranges from -1 to 1, where 1 indicates a perfect positive relationship, 0 indicates no relationship, and -1 indicates a perfect negative relationship.

9. Odds Ratio:

   - A measure of effect size for binary variables, representing the ratio of the odds of an event occurring in one group compared to another.

   - Useful for comparing the likelihood of an event between groups.

10. Statistical Power:

    - Definition: The probability of correctly rejecting the null hypothesis when it is false.

    - Influenced by sample size, effect size, and significance level.

    - Important for determining the likelihood of detecting a true effect in a study.

11. Hypothesis Testing Decision

    •   - Decision: Reject or fail to reject the null hypothesis based on the calculated test statistic and predetermined significance level.