Experimental: Random control trials (gold standard)
Qualitative: Thematic analysis, interviews, focus groups
Quantitative: Uses numerical data to measure and test theories,
Statistical concepts
P-values
A p-value < 0.05 typically indicates a statistically significant difference
A lower p-value suggests stronger evidence against the null hypothesis
Confidence intervals (CI)
A 95% CI means there’s a 95% chance the true value lies within that range
Narrow CI = more precise results; wide CI = more variability
Reliability and validity
Reliability: Consistency of a test (test-retest, interrater, intrarater)
Intrarater: the same clinician performs the test-retest
Interrater: different clinicians perform the test-retest
Validity: Accuracy—does the tool measure what it’s intended to?
Construct validity, content validity, and criterion validity.
Sensitivity, specificity, predictive values
Sensitivity: True positives — Rules out a condition (SnNOUT)
Sensitivity: False negative – indicates the absence of a condition when it is actually present.
Specificity: True negatives — Rules in a condition (SpPIN)
Positive predictive value (PPV) — Likelihood that a positive test is correct
Specificity: False positive – indicates the presence of a condition when it is actually absent.
Negative predictive value (NPV) — Likelihood that a negative test is correct
Measurement scales
Nominal: Categories without order (e.g., gender, blood type)
