Questions and Answers
What is Statistical Conclusion Validity?
The extent to which the study can detect that relationships or effects exist.
Which of the following are types of Statistical Conclusion Validity? (Select all that apply)
What does Lower Power in a study indicate?
Low probability of detecting that any differences exist.
Which factors negatively affect statistical power? (Select all that apply)
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How does Variability in Experimental Procedures affect a study?
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What does Heterogeneity in the Participants influence?
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What impact do Unreliable Measures have on research results?
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What are Artifacts in research?
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What is the effect of Multiple Comparisons on statistical tests?
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Study Notes
Statistical Conclusion Validity
- Represents the ability of a study to accurately identify relationships or effects.
- Essential for determining if results are meaningful and not due to chance.
Types of Statistical Conclusion Validity
- Low Power: Reduces the likelihood of detecting real differences; limits study findings.
- Variability in Experimental Procedures: Greater variability leads to increased error, diminished effect size, and lower power.
- Heterogeneity in Participants: Diversity among participants can amplify individual differences, affecting overall variability and power.
- Unreliable Measures: Inconsistent measures result in higher variability of responses; undermines effect size and power.
- Artifacts: Extraneous influences that may skew results and elevate error rates, reducing effect size and power.
- Multiple Comparisons: A higher number of statistical tests leads to increased Type I error risk, falsely rejecting the null hypothesis.
Lower Power
- Indicates a low chance of identifying actual differences in a study.
- Corresponds to a low likelihood of rejecting a false null hypothesis, compromising the study's validity.
Factors Affecting Power Negatively
- Decrease in Effect Size: A smaller effect reduces the probability of detecting significant differences.
- Decrease in Sample Size: Fewer participants lead to less robust results and lower power.
- Two-Tailed vs. One-Tailed Tests: A two-tailed test is less sensitive, potentially resulting in lower power compared to a more liberal one-tailed test.
- Decrease in Alpha Level: Lower significance thresholds (p-values) reduce the chances of finding significant results.
- Increased Variability: Greater error in data enhances uncertainty, leading to smaller effective sizes and reduced power.
Variability in Experimental Procedures
- Greater experimental variability correlates with higher error, influencing the reliability of results.
Heterogeneity in Participants
- Increased participant diversity can introduce variability that complicates the effectiveness of the study's findings.
Unreliable Measures
- Variability in measurement methods leads to inconsistent data, affecting the integrity of effect size and overall validity.
Artifacts
- External factors introduce errors, complicating the results and reducing the reliability of effect size measurements.
Multiple Comparisons
- Performing multiple statistical tests on the same data increases the likelihood of false positives (Type I error) independent of effect size considerations.
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Description
Explore essential concepts of Statistical Conclusion Validity through these informative flashcards. Learn about types of validity, the role of power, and potential errors that can affect study outcomes. This resource is perfect for students looking to master critical statistical concepts.
