Chapter 11 Questions

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18 Terms

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Sample Size Calculation Importance

Ensures clear and trustworthy results, helps detect real differences, avoids wasting resources, prevents using too few or too many participants, and ensures applicability to the larger population.

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Why start with more participants than sample size tables recommend?

To account for dropouts or non-completers, ensuring enough participants to draw valid conclusions.

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Type I Error

A false positive; believing there’s a difference when there isn’t.

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Type II Error

A false negative; believing there’s no difference when there actually is.

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P-value

The chance of making a Type I error; researchers typically aim for P < 0.05 for statistical significance.

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Power of a Study

The probability of correctly detecting a real effect, typically aimed to be 80% or 90%.

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Informed Consent

Participants must be clearly informed about the study’s purpose, risks, and benefits.

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Efficacy

How well a treatment works in controlled conditions, like a lab.

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Effectiveness

How well a treatment works in real-world settings.

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Internal Validity

Confidence that the study results are due to the intervention, not other factors.

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External Validity

How well the results apply to the general population outside the study.

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Four Phases of Drug Testing

  1. Phase I: Tests safety; 2. Phase II: Tests efficacy; 3. Phase III: Large trials for effectiveness; 4. Phase IV: Ongoing monitoring.
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Randomized Controlled Trials (RCTs) Purpose

  1. Compare treatments; 2. Eliminate bias through random assignment.
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Ethical Considerations in RCTs

Randomization must be ethical, informed consent is essential, placebos should not replace proven treatments, and trials need clear stop rules.

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Gold Standard for Evaluating Treatments

Randomized controlled trials are considered the gold standard.

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Importance of Clinical Trial Registration

Ensures transparency, prevents publication bias, avoids duplication, builds public trust, and adheres to ethical standards.

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MRFIT Study Purpose

To assess if modifying risk factors reduces heart disease deaths.

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MRFIT Study Findings

Risk factors improved in the intervention group, but there was no significant difference in death rates.