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Medical Research: Validity, Bias, Confounds, Probability, P-value, and Power

Medical Research: Validity, Bias, Confounds, Probability, P-value, and Power. Kimberly Pendell. Validity. Validity implies that the measurement reliably measures what it intends to measure. Validity relates to the magnitude of bias. Bias.

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Medical Research: Validity, Bias, Confounds, Probability, P-value, and Power

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  1. Medical Research: Validity, Bias, Confounds, Probability, P-value, and Power Kimberly Pendell

  2. Validity • Validity implies that the measurement reliably measures what it intends to measure. • Validity relates to the magnitude of bias.

  3. Bias • Bias is the systematic tendency to produce an outcome that differs from the underlying truth.

  4. Some different types of bias • Selection bias • Interviewer bias • Recall bias • Detection bias • Verification bias • Publication bias

  5. Bias and Research Designs

  6. Confounds • A confound is a variable that is distributed differently in the study group and the control group and that affects the outcome being assessed. (Riegelman, 2005) • Confounding variables can appear through bias or random chance.

  7. Probability • Probability is the quantitative estimate of the likelihood of condition existing (as in a diagnosis) or of subsequent events (such as in a treatment study). (Guyatt, Rennie, 2002)

  8. P-value • P-value is the probability of an outcome occurring by chance. The p-value establishes statistical significance. • In medical research the standard p-value is p<.05

  9. Power • Power is the ability of a research study to demonstrate statistical significance. • Researchers determine the size of study population needed for a set amount of power before beginning their study. • If the study has statistically significant results, the study has enough power. • In medical research the standard for power is 80%.

  10. Readings: • Guyatt, G., Rennie, D. (Eds.). (2002). Users’ guide to the medical literature: a manual for evidence-based practice. USA: American Medical Association. • Katzer, J., Cook, K.H., Crouch, W.W. (1998). Evaluating information: a guide for users of social science research. Massachusetts: McGraw Hill. • Riegelman, R.K. (2005). Studyinga study & testing a test: how to read the medical evidence. Philadelphia, PA: Lippincott Williams & Wilkins.

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