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Questions and Answers
What is the standard alpha level typically set in psychology for significance testing?
What is a Type I error also known as?
What is a Type II error also known as?
What does power (1-β) represent in hypothesis testing?
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In hypothesis testing, what is the null hypothesis (H0)?
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What does NHST stand for?
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NHST allows for gray areas in interpreting results.
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We typically set α = ______ in psychology.
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What does Bayesian analysis help us adjust in relation to outcomes?
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Which hypothesis represents the possibility of a difference being due to random chance?
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In hypothesis testing, what should we compare the obtained p-value against?
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Which of the following represents a two-tailed hypothesis test?
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What is the first step in hypothesis testing according to the framework outlined?
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What is determined after calculating the probability of finding a sample mean difference assuming the null hypothesis is true?
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When conducting hypothesis testing, what do we conclude when the p-value exceeds the set significance level?
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Which option best represents a one-tailed hypothesis test?
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What do we aim to find out by using hypothesis testing?
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If α is set at 0.05, what does this imply regarding the probability of a Type I error?
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Which of the following statements about power in hypothesis testing is accurate?
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What scenario might prompt a researcher to set a lower alpha level than the standard?
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What does frequentist hypothesis testing focus on?
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What type of error is represented by failing to find a difference when one actually exists?
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Which of the following best describes a 'true positive' outcome in hypothesis testing?
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In the context of hypothesis testing, what is likely a common characteristic of the 5% significance level?
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Study Notes
Hypothesis Testing Overview
- Hypothesis testing determines the probability of obtaining data under the assumption that the null hypothesis (H0) is true.
- Common standard for significance testing in psychology is set at α = 0.05, indicating a 5% chance of making a Type I error (false positive).
Types of Errors
- Type I Error (α): Incorrectly rejecting the null hypothesis when it is true (false positive).
- Type II Error (β): Not rejecting the null hypothesis when it is false (false negative).
- Power of a test (1 - β) indicates the ability to detect a true effect when it exists; commonly aimed at β = 0.20.
Key Concepts in Hypothesis Testing
- Statistical power and significance levels can be adjusted based on the context or the risks of false positives versus false negatives.
- Frequentist approach (NHST): Accepts a predetermined level of chance; results are either significant or not.
- Bayesian approach: Updates confidence based on prior probabilities and current data; allows for more nuance in understanding outcomes.
Steps in Hypothesis Testing
- Establish Research Question: E.g., Determine if an instructor is more effective with undergraduates or graduates.
- Obtain Random Samples: Collect data under each condition for comparison.
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Set Hypotheses:
- Null Hypothesis (H0): No difference between populations.
- Alternative Hypothesis (H1): Indicates a difference; can be one-tailed or two-tailed based on expected outcomes.
- Calculate Probability: Determine the mean and evaluate the probability of observing the data assuming H0 is true.
- Compare p-value to α: Decide whether to reject H0 based on significance threshold.
Misunderstandings in Hypothesis Testing
- Key question in hypothesis testing is "What is the probability of observing this data given that the null hypothesis is true?" not the reversed implication regarding the truth of the null hypothesis.
Terminology Caution
- Avoid stating results in terms of "proving" or "accepting" hypotheses; use "reject" or "fail to reject" instead.
Hypothesis Testing Overview
- Utilizes statistics to evaluate hypotheses by determining the probability of obtaining data under the null hypothesis.
- In psychology, a standard significance level is commonly set at 5% (α = 0.05).
Possible Outcomes of Hypothesis Tests
- True Positive: Correctly identifies a real difference.
- False Positive (Type I Error): Incorrectly identifies a difference that does not exist.
- False Negative (Type II Error): Fails to detect a difference when one is present.
- True Negative: Correctly identifies that no difference exists.
Alpha, Beta, and Power
- Alpha (α): Probability of a Type I Error; typically set at 0.05 in psychological studies.
- Beta (β): Probability of a Type II Error; often aimed to be 0.20.
- Power (1 - β): The likelihood of correctly detecting a true difference in the population.
Significance Level Decisions
- Adjustments to α can minimize false positives or false negatives depending on the study context.
Hypothesis Testing Approaches
-
Frequentist Approach (NHST):
- Assumes a fixed chance of Type I Error (usually 5%).
- Results are classified as either significant or not.
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Bayesian Approach:
- Incorporates prior data and adjusts the probability of an outcome based on new evidence.
- Allows for degrees of certainty regarding the effectiveness of treatments based on prior knowledge.
Steps in Hypothesis Testing
- Establish a question to investigate (e.g., instructor effectiveness for different student levels).
- Collect random samples under each condition (e.g., ratings from undergraduates and graduates).
- Set up the null hypothesis (H0): No difference in ratings across groups.
- Define the alternative hypothesis (H1), which can be either two-tailed (differences exist) or one-tailed (specific direction).
Calculating the Probability
- Determine the mean differences between samples.
- Calculate the probability (p-value) of observing such differences if H0 is true.
- Compare the p-value to the significance threshold (α). If the p-value is lower than α, reject H0.
Importance of Hypothesis Testing
- Helps in distinguishing between results due to chance and those reflecting real effects in experimental contexts.
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Description
Dive into the essential concepts of hypothesis testing in psychology with Psyc610. This quiz covers key elements such as the null hypothesis, significance testing, and the Z distribution. Understand the importance of probability in evaluating hypotheses and its role in statistical analysis.
