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Questions and Answers
What does the null hypothesis (H₀) state?
What does the null hypothesis (H₀) state?
- There is a significant difference between groups.
- The data supports the predicted outcome.
- The effect exists.
- The predicted effect does not exist. (correct)
A Type I error occurs when a false positive is concluded.
A Type I error occurs when a false positive is concluded.
True (A)
What are the two types of hypothesis tests discussed?
What are the two types of hypothesis tests discussed?
Null Hypothesis (H₀) and Alternative Hypothesis (H₁)
The probability of observing the data if the null hypothesis is true is called the ______.
The probability of observing the data if the null hypothesis is true is called the ______.
Match the following errors with their definitions:
Match the following errors with their definitions:
Which statement is true regarding one-tailed and two-tailed hypotheses?
Which statement is true regarding one-tailed and two-tailed hypotheses?
An effect size quantifies the magnitude of an observed effect.
An effect size quantifies the magnitude of an observed effect.
What is the main focus of Fisher's argument regarding statistical significance?
What is the main focus of Fisher's argument regarding statistical significance?
The confidence level typically used for statistical significance in hypothesis testing is ______.
The confidence level typically used for statistical significance in hypothesis testing is ______.
Which of the following tests is used to compare the means of two groups?
Which of the following tests is used to compare the means of two groups?
Flashcards
Parameters
Parameters
Values estimated from data that represent truths about relationships between variables. They are typically constants.
Null Hypothesis (H₀)
Null Hypothesis (H₀)
The statement that your expected effect does not exist or is not present in the data. It assumes no relationship or change.
Alternative Hypothesis (H₁)
Alternative Hypothesis (H₁)
The statement that your predicted effect exists. It claims a relationship or change.
One-tailed Hypothesis
One-tailed Hypothesis
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Two-tailed Hypothesis
Two-tailed Hypothesis
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Type I Error
Type I Error
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Type II Error
Type II Error
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p-value
p-value
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Effect Size
Effect Size
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Power
Power
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Study Notes
Parameters and Variables
- Parameters are estimated from data instead of being measured, often representing truths about relations between variables.
- Examples of parameters include means, medians, correlation coefficients, and regression coefficients.
Outcome Variables
- Outcome variable (denoted by b) represents a predicted outcome from one or more variables (X).
- Outcomes can be predicted from single or multiple variables.
Hypothesis Testing
- Null Hypothesis (H₀): Predicts no effect or a lack of difference. (e.g., no difference in anxiety levels if you imagine a presentation).
- Alternative Hypothesis (H₁): Predicts an effect or difference. (e.g., anxiety levels will change if you imagine a presentation).
Hypothesis Types
- Directional (one-tailed) hypothesis specifies the direction of the effect. (e.g., anxiety will increase when imagining a presentation). If the results are in the opposite direction you must ignore them.
- Non-directional (two-tailed) hypothesis doesn't specify direction. (e.g., anxiety will change when imagining a presentation).
Errors in Hypothesis Testing
- Type I error: Concluding there's an effect when there isn't. (false positive).
- Type II error: Concluding there's no effect when there is. (false negative).
- Alpha (α-level): Probability of a Type I error, commonly set to .05 or 5%.
- Beta (β-level): Probability of a Type II error.
Statistical Significance
- p-value: Probability of obtaining results as extreme as or more extreme than those observed if there is no effect.
- Statistical significance (p<.05): A result is statistically significant if the p-value is less than .05.
Effect Size
- Effect Size: A standardized measure of the magnitude of an effect, like Cohen's d, Glass' g, or Pearson's correlation coefficient.
- It provides a quantitative measure of the observed effect’s importance
Statistical Power
- Power: The ability of a test to detect a genuine effect.
- Power is 1 - beta (1 - β).
Correlation
- Pearson's r: measures the strength and direction of the linear relationship between two variables.
Meta-analysis
- Meta-analysis: Combines the results of multiple studies to draw more accurate conclusions.
t-tests
- t-tests compare means of two groups or conditions.
- Independent samples t-test: used for independent groups.
- Paired samples t-test: used for dependent groups.
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Description
Explore the crucial concepts of parameters, outcome variables, and hypothesis testing in statistics. This quiz covers definitions, types of hypotheses, and examples, helping you understand how these ideas relate to data analysis. Perfect for students looking to solidify their knowledge in statistics.
