1
Q
Term
A
Definition
2
Q
Variable
A
Any characteristic or attribute that can vary among subjects in a study.
3
Q
Independent variable
A
The variable that is manipulated or categorized to observe its effect on the dependent variable.
4
Q
Dependent variable
A
The outcome variable measured to assess the effect of the independent variable.
5
Q
Confounding variable
A
An extraneous variable that affects both the independent and dependent variables
6
Q
Bias
A
Systematic error that leads to an incorrect estimate of effect or association.
7
Q
Selection bias
A
Bias arising when study participants are not representative of the target population
8
Q
Information bias
A
Bias caused by errors in measuring exposure
9
Q
Random error
A
Variability in data due to chance
10
Q
Cohort study
A
An observational study following a group of individuals over time to assess exposure-outcome relationships.
11
Q
Case-control study
A
An observational study comparing individuals with a condition (cases) to those without (controls) to identify potential risk factors.
12
Q
Cross-sectional study
A
An observational study measuring exposure and outcome at a single point in time to assess prevalence or associations.
13
Q
Experimental study
A
A study in which researchers actively manipulate variables to assess causal effects
14
Q
Randomized controlled trial (RCT)
A
An experimental study where participants are randomly assigned to intervention or control groups to reduce bias and establish causality.
15
Q
Blinding
A
Keeping participants
16
Q
Placebo effect
A
Improvement in a participant’s condition due to expectations rather than the active intervention.
17
Q
Internal validity
A
The extent to which study results accurately reflect the true relationship between exposure and outcome within the study population.
18
Q
External validity
A
The extent to which study findings can be generalized to other populations
19
Q
Confounding
A
Distortion of the apparent effect of an exposure on an outcome caused by a third variable associated with both.
20
Q
Control of confounding
A
Methods to reduce confounding
21
Q
Data presentation
A
The organized display of research data in tables
22
Q
Frequency distribution
A
A summary of how often each value occurs in a dataset; often displayed in a table or histogram.
23
Q
Histogram
A
A graphical representation of the distribution of numerical data using bars to show frequency of values within intervals.
24
Q
Bar graph
A
A chart that uses bars to compare categorical data; bar heights represent the magnitude of each category.
25
Pie chart
A circular graph divided into sectors representing the proportion of each category relative to the whole.
26
Scatter plot
A graph showing the relationship between two quantitative variables
27
Line graph
A graph connecting data points with lines to show trends over time or continuous variables.
28
Box-and-whisker plot
A visual summary of data showing the median
29
Mean
The arithmetic average of a set of numbers
30
Median
The middle value in a dataset when values are arranged in order; separates the lower and upper halves.
31
Mode
The most frequently occurring value in a dataset.
32
Range
The difference between the maximum and minimum values in a dataset
33
Standard deviation (SD)
A measure of the dispersion of data points around the mean; smaller SD indicates more clustered data.
34
Standard error (SE)
An estimate of the variability in the sample mean if multiple samples were drawn from the population.
35
Confidence interval (CI)
A range of values within which the true population parameter is likely to fall
36
p-value
The probability of observing results as extreme as those in the study if the null hypothesis is true; p < 0.05 is typically considered statistically significant.
37
Statistical significance
A result unlikely to have occurred by chance alone
38
Effect size
A quantitative measure of the magnitude of a phenomenon
39
Correlation coefficient (r)
A measure of the strength and direction of a linear relationship between two variables
40
Regression analysis
A statistical method to model the relationship between a dependent variable and one or more independent variables.
41
Clinical relevance
The practical importance of study findings in improving patient care
42
Evidence-based practice (EBP)
Clinical decision-making that integrates the best research evidence
43
Data interpretation
The process of analyzing results to draw meaningful conclusions and identify patterns
44
Reporting results
The clear
45
Translational application
Applying research findings to real-world clinical practice or public health interventions to improve outcomes.
46
Research design
The overall strategy and structure of a study that defines how data will be collected
47
Observational study
A study where researchers observe outcomes without intervening
48
Systematic review
A structured synthesis of all relevant studies on a specific question using predefined criteria to minimize bias.
49
Meta-analysis
A statistical technique that combines results from multiple studies to produce a pooled estimate of effect.
50
Critical appraisal
The systematic evaluation of research evidence to assess its validity
51
Levels of evidence
A hierarchy ranking study designs by their ability to produce reliable evidence
52
Publication bias
The tendency for studies with positive or significant results to be published more often than studies with negative or null findings.
53
Sample size calculation
The process of determining the number of participants needed to detect a meaningful effect with adequate statistical power.
54
Power (statistical)
The probability that a study will detect a true effect if it exists; influenced by sample size
55
Null hypothesis (H₀)
The default assumption that there is no effect or difference between groups in a study.
56
Alternative hypothesis (H₁)
The hypothesis that there is a true effect or difference between groups
57
Statistical test
A method to determine whether observed data are consistent with a null hypothesis
58
Parametric test
A statistical test that assumes data follow a specific distribution (usually normal)
59
Non-parametric test
A statistical test that does not assume a specific distribution
60
t-test
Compares the means of two groups to determine if the difference is statistically significant.
61
Paired t-test
Compares means from the same group at two time points or under two conditions.
62
ANOVA (Analysis of Variance)
Compares means across three or more groups to test for overall differences.
63
Chi-square test
Tests for an association between two categorical variables.
64
Fisher’s exact test
A test for association between categorical variables used when sample sizes are small.
65
Correlation
A measure of the strength and direction of the linear relationship between two variables (does not imply causation).
66
Causation
A relationship where one variable directly influences another
67
Relative risk (RR)
The ratio of the probability of an event occurring in the exposed group compared to the unexposed group.
68
Odds ratio (OR)
The ratio of the odds of an event occurring in one group to the odds in another group
69
Sensitivity
The ability of a test to correctly identify those with the condition (true positives).
70
Specificity
The ability of a test to correctly identify those without the condition (true negatives).
71
Positive predictive value (PPV)
The probability that a person with a positive test truly has the condition.
72
Negative predictive value (NPV)
The probability that a person with a negative test truly does not have the condition.
73
Likelihood ratio (LR)
Combines sensitivity and specificity to indicate how much a test result changes the odds of having a disease.
74
Clinical significance
The practical importance of a finding in improving patient outcomes
75
Number needed to treat (NNT)
The number of patients who need a treatment to prevent one adverse outcome or achieve one beneficial outcome.
76
Number needed to harm (NNH)
The number of patients who need to receive a treatment before one experiences a harmful side effect.
77
Forest plot
A graphical representation of results from multiple studies
78
Funnel plot
A plot used to detect publication bias in meta-analyses
79
Kaplan-Meier curve
A survival plot showing the proportion of individuals surviving over time in different groups.
80
Hazard ratio (HR)
The ratio of the hazard rates between two groups in a survival analysis
81
Intention-to-treat analysis
Analyzing study participants according to their original group assignment
82
Evidence synthesis
Combining results from multiple studies to form a comprehensive understanding of a clinical question.
83
Clinical practice guideline (CPG)
Systematically developed recommendations to assist clinician and patient decision-making for specific conditions.
84
Guideline grading (e.g.
GRADE)
85
Quality improvement (QI)
Systematic
86
Plan-Do-Study-Act (PDSA) cycle
A framework for testing and implementing changes in clinical practice through iterative cycles.
87
Benchmarking
Comparing performance metrics to standards or best practices to identify areas for improvement.
88
Root cause analysis (RCA)
A method to identify underlying causes of errors or adverse events to prevent recurrence.
89
Clinical decision-making
The process of evaluating evidence
90
Shared decision-making
A collaborative process where clinicians and patients make healthcare decisions together
91
Risk-benefit analysis
Evaluating potential benefits versus harms of an intervention to guide clinical decisions.
92
Cost-effectiveness analysis (CEA)
Comparing the relative costs and outcomes (effects) of two or more interventions.
93
Health technology assessment (HTA)
A systematic evaluation of the properties
94
Patient-reported outcome (PRO)
Any report on a patient’s health status coming directly from the patient
95
Evidence-based guideline implementation
The process of putting clinical guideline recommendations into routine practice to improve care quality.
96
Clinical audit
A systematic review of care against explicit standards to improve patient outcomes.
97
Continuous quality improvement (CQI)
Ongoing
98
Performance metrics
Quantitative measures used to assess efficiency
99
Knowledge translation
The process of taking research findings and ensuring they are applied in clinical practice and policy.
100
Evidence-based policy
Health policies developed using the best available research evidence to maximize population outcomes.
101
Clinical impact factor
The measure of how strongly a study’s findings can influence clinical practice.
102
Implementation science
The study of methods to promote the uptake of research findings and evidence-based practices into routine healthcare.
103
Barriers to guideline adherence
Factors that prevent clinicians or organizations from following evidence-based recommendations
104
Facilitators of guideline adherence
Factors that support the adoption of evidence-based practices
105
Translational research (T1/T2/T3/T4)
The process of moving research from basic science (T1) to human application (T2)
106
Outcome measurement
The systematic assessment of patient or population results to evaluate the effectiveness of interventions or programs.
107
Type I error (false positive)
Rejecting the null hypothesis when it is actually true.
108
Type II error (false negative)
Failing to reject the null hypothesis when it is actually false.
109
Alpha level
The threshold probability for Type I error
110
Beta level
The probability of making a Type II error
111
Precision
The degree to which repeated measurements under unchanged conditions show the same results.
112
Accuracy
The degree to which a measurement reflects the true value.
113
Validity
The extent to which an instrument measures what it intends to measure.
114
Reliability
The consistency or repeatability of a measurement over time or between observers.
115
Measurement bias
Systematic error due to inaccurate measurement methods or instruments.
116
Recall bias
Bias that occurs when participants do not remember previous events accurately.
117
Observer bias
Bias that arises when the researcher’s expectations influence data collection or interpretation.
118
Attrition bias
Bias resulting from loss of participants during a study
119
Placebo control
A control group that receives an inactive treatment to measure the true effect of an intervention.
120
Crossover study
Study design where participants receive both the treatment and control at different times.
121
Cluster randomization
Randomization by group or setting rather than by individual.
122
Factorial design
Study design testing more than one intervention simultaneously in various combinations.
123
Blinding (single/double/triple)
Levels of masking knowledge about group assignments to prevent bias.
124
Intention-to-treat principle
Analyzing all participants in the groups they were originally assigned
125
Per-protocol analysis
Analyzing only participants who completed the study exactly as designed.
126
Attrition
Loss of participants during follow-up in a study.
127
Generalizability
The degree to which study findings apply to other populations or settings.
128
Internal consistency
A measure of how well items within a test assess the same construct.
129
Cronbach’s alpha
A statistic used to measure internal consistency or reliability of a scale.
130
Face validity
The degree to which a test appears to measure what it claims to measure.
131
Construct validity
The degree to which a test measures the theoretical construct it intends to measure.
132
Content validity
The extent to which a measurement represents all aspects of a given construct.
133
Criterion validity
The degree to which a measure correlates with an established external criterion.
134
Concurrent validity
How well a test correlates with a benchmark measured at the same time.
135
Predictive validity
How well a test predicts future performance or outcomes.
136
Standardization
Establishing consistent procedures for administration and scoring of a measure.
137
Norm-referenced test
A test comparing an individual’s score to a representative group’s performance.
138
Criterion-referenced test
A test measuring performance against a fixed standard or specific skill.
139
Data cleaning
The process of identifying and correcting errors
140
Coding
The process of categorizing and assigning numeric or textual codes to data for analysis.
141
Outlier
A data point significantly different from others
142
Normal distribution
A symmetric
143
Skewness
The measure of asymmetry in a data distribution.
144
Kurtosis
The measure of the “tailedness” or peakedness of a distribution.
145
Transformation
Mathematical modification of data to meet assumptions for statistical tests.
146
Log transformation
Converting skewed data using logarithms to approximate normality.
147
Z-score
The number of standard deviations a data point is from the mean.
148
Percentile
A value below which a given percentage of observations fall.
149
Quartile
One of three points dividing a data set into four equal parts.
150
Interquartile range (IQR)
The range between the first (Q1) and third (Q3) quartiles
151
Variance
The average of the squared differences from the mean; shows data spread.
152
Covariance
A measure of how two variables change together.
153
Linear regression
A model predicting a continuous outcome from one or more predictors.
154
Logistic regression
A model predicting a binary outcome (e.g.
155
Multivariate analysis
Statistical analysis involving multiple dependent or independent variables.
156
Survival analysis
A statistical method analyzing time-to-event data
157
Hazard function
The instantaneous risk of an event occurring at a specific time point.
158
Proportional hazards model
A model assuming the ratio of hazard functions between groups is constant over time.
159
Censoring
Occurs in survival data when the event of interest has not happened by the end of observation.
160
Life table
A table summarizing survival data across time intervals.
161
Kaplan-Meier estimator
A nonparametric method to estimate survival probability over time.
162
Cox regression
Regression model used to examine associations between predictors and survival time.
163
Prognostic factor
A variable associated with future outcomes in a disease or condition.
164
Predictive factor
A variable that modifies the effect of a treatment on an outcome.
165
Receiver operating characteristic (ROC) curve
A plot showing trade-offs between sensitivity and specificity at different thresholds.
166
Area under the curve (AUC)
A measure of overall diagnostic test accuracy; higher values indicate better performance.
167
Cut-off value
The threshold score used to distinguish between positive and negative test results.
168
Reference range
The range of values for a physiological measurement in healthy individuals.
169
Z-test
A statistical test for comparing population means when standard deviation is known.
170
F-test
A test comparing variances across groups in ANOVA.
171
Post-hoc test
Analysis following ANOVA to identify specific group differences.
172
Bonferroni correction
A method to control Type I error when conducting multiple comparisons.
173
False discovery rate (FDR)
The expected proportion of false positives among significant results.
174
What are descriptive statistics?
Summarize and describe the main features of a dataset using measures like mean, median, mode, and standard deviation.
175
What is inferential statistics?
Use sample data to make generalizations or predictions about a population.
176
Define parametric data.
177
What is non-parametric data?
Data that do not meet assumptions of normality; analyzed using rank-based tests.
178
What is nominal data?
Categorical data without an intrinsic order (e.g., gender, blood type).
179
What is ordinal data?
Categorical data with a defined order but unequal intervals (e.g., pain scale ratings).
180
What is interval data?
Numerical data with equal intervals but no true zero point (e.g., temperature in °C).
181
What is ratio data?
Numerical data with equal intervals and a true zero (e.g., weight, height).
182
What is cross-tabulation?
Table showing relationships between two categorical variables.
183
What is a contingency table?
A table used in chi-square tests to display frequency distributions of variables.
184
Define degrees of freedom.
185
What is assumption testing?
Checking if data meet statistical test requirements (e.g., normality, homogeneity of variance).
186
What does homogeneity of variance refer to?
Assumption that different groups have similar variance in data.
187
What is Levene’s test?
A test for equality of variances between groups.
188
What are parametric assumptions?
Requirements for parametric tests, including normality and equal variances.
189
Define effect modification.
190
What is an interaction term in regression?
A variable in regression representing combined effects of two or more predictors.
191
What is multicollinearity?
When predictor variables in regression are highly correlated, reducing model reliability.
192
Define residual in regression analysis.
193
What is goodness of fit?
How well a model’s predicted values match the observed data.
194
What does R-squared (R²) represent?
Proportion of variance in the dependent variable explained by the independent variable(s).
195
What is adjusted R-squared?
Modified R² accounting for the number of predictors in a model.
196
What is model validation?
Assessing a statistical model’s performance on new or independent data.
197
What is overfitting?
When a model describes random noise instead of the underlying relationship.
198
What is underfitting?
When a model is too simple to capture data relationships.
199
What is cross-validation?
A method for testing a model’s predictive performance by splitting data into training and test sets.
200
Define bootstrap sampling.
201
What is a Monte Carlo simulation?
A computational method using repeated random sampling to model uncertainty.
202
What is sensitivity analysis?
Testing how results change when assumptions or inputs are varied.
203
What is missing data?
Instances where observations are not recorded for some variables.
204
What is imputation?
The process of replacing missing data with substituted values.
205
What is multiple imputation?
Using multiple models to estimate and replace missing data while preserving variability.
206
What does data transparency mean?
Ensuring all data, code, and analyses are accessible for verification.
207
Define reproducibility in research.
208
What is replication in research?
Repeating a study independently to confirm findings.
209
What is open science?
The practice of making research methods and data openly available to enhance credibility.
210
What does ethical approval entail?
Formal review by an ethics board to ensure participant rights and safety.
211
What is informed consent?
Voluntary agreement to participate after understanding study purpose, risks, and benefits.
212
What is anonymity in research?
Ensuring participants cannot be personally identified from data.
213
What is confidentiality?
Protecting participant data from unauthorized access.
214
What is data storage?
Securely maintaining research data for future verification or analysis.
215
What is data sharing?
Providing access to datasets for transparency or collaboration.
216
What are authorship criteria?
Guidelines determining who qualifies as an author based on contribution.
217
What is a conflict of interest?
Any factor that could influence a researcher’s objectivity.
218
What is peer review?
Evaluation of research by independent experts before publication.
219
Define research integrity.
220
What is evidence synthesis?
Combining results from multiple studies to form a comprehensive understanding of a clinical question.
221
What is a clinical practice guideline (CPG)?
Systematically developed recommendations to assist clinician and patient decision-making for specific conditions.
222
What is guideline grading (e.g., GRADE)?
A system to rate the quality of evidence and strength of recommendations in clinical guidelines.
223
What is quality improvement (QI)?
Systematic, data-driven efforts to improve healthcare processes, patient outcomes, and efficiency.
224
What is the Plan-Do-Study-Act (PDSA) cycle?
A framework for testing and implementing changes in clinical practice through iterative cycles.
225
What is benchmarking?
Comparing performance metrics to standards or best practices to identify areas for improvement.
226
What is root cause analysis (RCA)?
A method to identify underlying causes of errors or adverse events to prevent recurrence.
227
What is clinical decision-making?
The process of evaluating evidence, patient values, and clinical expertise to choose the best course of action.
228
What is shared decision-making?
A collaborative process where clinicians and patients make healthcare decisions together, considering evidence and patient preferences.
229
What is risk-benefit analysis?
Evaluating potential benefits versus harms of an intervention to guide clinical decisions.
230
What is cost-effectiveness analysis (CEA)?
Comparing the relative costs and outcomes (effects) of two or more interventions.
231
What is health technology assessment (HTA)?
A systematic evaluation of the properties, effects, and impacts of healthcare technologies, including medical devices, drugs, and procedures.
232
What is a patient-reported outcome (PRO)?
Any report on a patient’s health status coming directly from the patient, without interpretation by clinicians.
233
What is evidence-based guideline implementation?
The process of putting clinical guideline recommendations into routine practice to improve care quality.
234
What is a clinical audit?
A systematic review of care against explicit standards to improve patient outcomes.
235
What is continuous quality improvement (CQI)?
Ongoing, iterative efforts to improve healthcare processes and outcomes using data and feedback loops.
236
What are performance metrics?
Quantitative measures used to assess efficiency, effectiveness, and quality in healthcare delivery.
237
What is knowledge translation?
The process of taking research findings and ensuring they are applied in clinical practice and policy.
238
What is evidence-based policy?
Health policies developed using the best available research evidence to maximize population outcomes.
239
What is a clinical impact factor?
The measure of how strongly a study’s findings can influence clinical practice.
240
What is implementation science?
The study of methods to promote the uptake of research findings and evidence-based practices into routine healthcare.
241
What are barriers to guideline adherence?
Factors that prevent clinicians or organizations from following evidence-based recommendations, such as lack of knowledge, resources, or motivation.
242
What are facilitators of guideline adherence?
Factors that support the adoption of evidence-based practices, including education, decision support tools, and leadership engagement.
243
What is translational research (T1/T2/T3/T4)?
The process of moving research from basic science (T1) to human application (T2), clinical practice (T3), and population health impact (T4).
244
What is outcome measurement?
The systematic assessment of patient or population results to evaluate the effectiveness of interventions or programs.
Biochemistry
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