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Introduction to Hypothesis Testing Study Guide

This guide explains the fundamental concepts of confidence intervals in biostatistics. It covers statistical inference, the distinction between population parameters and point estimates, and the construction of confidence intervals. Key elements such as the z-score, standard error of the mean (SEM), and variance are detailed. The guide also discusses how sample size, data variance, and confidence level affect the precision of an interval, providing a practical example to illustrate these princip

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Hypothesis Testing and Statistical Power Analysis

Hypothesis Testing and Statistical Significance

This module covers hypothesis testing, null and alternate hypotheses, Type I and Type II error, p-values, and confidence intervals.

Null Hypothesis
Alternate Hypothesis
Hypothesis Testing
Type I Error
Type II Error
p value
Statistical significance
Confidence Interval

Statistical Power and Sample Size Estimation

This module covers statistical power, the four pillars of power, a priori sample size estimation, and post-hoc power calculation.

Statistical Power
Alpha
Effect Size
Variance
Sample Size
A priori Sample Size Estimation
Post-hoc Power Calculation
Confidence Intervals

Statistical Concepts and Evidence-Based Practice

Null and Alternative Hypothesis Concepts

This video discusses the null hypothesis and the alternative hypothesis as two competing explanations used in hypothesis testing to determine if an intervention produced a different outcome or if differences are due to random variation.

null hypothesis
alternative hypothesis
hypothesis testing
random variation
evidence-based clinicians

Post-hoc Analysis and Confidence Interval Analysis

Post-hoc analysis methods include computing with the traditional Cohen approach or determining results through confidence interval analysis of the effect size.

Post-hoc Analysis
Cohen approach
Confidence Interval Analysis
Effect size
Clinically meaningful difference
Dichotomous result
Power determination

Evidence-Based Practice for Physical Therapy

The evidence-based practice process guides clinicians in interpreting results, applying best available evidence to clinical practice, and assessing outcomes of care.

evidence-based practice (EBP)
critical appraisal
clinical meaningfulness
statistical analyses
interpreting results
applying evidence to patient care
assessing outcomes of care

Statistical Concepts in Physical Therapy Literature

This commentary reviews statistical concepts and procedures used to identify between-group differences and describe attributes of aggregated data in physical therapy literature.

descriptive statistics
central tendency
mean
median
mode
variability
standard deviation
range
interquartile range
inferential statistical tests
parametric tests
nonparametric tests
hypothesis testing
null hypothesis
alpha level
P values
type I error
confidence intervals
point estimate
magnitude of treatment effect

Clinical Meaningfulness and Statistical Effect Sizes

This text discusses how clinicians use raw effect size, standardized effect size indices, minimal clinically important difference, and confidence intervals to determine if treatment effects are clinically meaningful.

raw effect size
standardized effect size indices
minimal detectable change (MDC)
minimal clinically important difference (MCID)
95% confidence interval (CI)
absolute risk reduction (ARR)
relative risk reduction (RRR)
number needed to treat (NNT)
systematic reviews
meta-analysis
forest plots
dichotomous outcomes

Integrating Evidence Into Clinical Practice

An evidence-based practitioner performs critical appraisal of literature to integrate synthesized evidence with clinical expertise and patient values.

critical appraisal
clinical practice guidelines
evidence-based practice
randomized controlled trials
clinical expertise
patient values
minimal clinically important difference
shared decision making
treatment recommendations
outcome measures

Foundations of Research Design and Statistical Analysis

Understanding Variables and Their Measurement

This lecture addresses the measurement classifications of variables and the functional roles variables play within empirical research models.

Categorical variables
Numerical variables
Nominal variables
Ordinal variables
Discrete variables
Continuous variables
Interval data
Ratio data
Independent variables
Dependent variables
Covariate

The Empirical Rule and Normal Distribution

This lecture introduces the normal distribution and the Empirical Rule to understand how continuous data clusters around a center point and spreads within standard deviation intervals.

Normal distribution
Empirical Rule
Continuous data
Discrete data
Measures of central tendency
Mean
Median
Mode
Standard deviation
Inflection point
Symmetry

Review of Research Design and Reasoning

This review covers internal and external validity, deductive and inductive reasoning, and various research designs including experimental, quasi-experimental, and descriptive studies.

internal validity
external validity
efficacy
deductive reasoning
inductive reasoning
descriptive studies
exploratory studies
experimental studies
randomized controlled trials
quasi experimental studies
single subject designs
case control studies
cohort studies
systematic reviews
meta analyses
prospective research
retrospective research
cross-sectional research
longitudinal research
case studies

Review of Psychometric Properties

A review of psychometric properties including reliability, validity, and responsiveness.

Reliability
Validity
Responsiveness
Interrater reliability
Intrarater reliability
Test-retest reliability
Face validity
Content validity
Criterion-related validity
Concurrent validity
Predictive validity
Construct validity
Minimal detectable change (MDC)
Minimal clinically important difference (MCID)
Measurement error
Floor effect
Ceiling effect

The Empirical Rule and Normal Distribution

The Empirical Rule and Normal Distribution describe continuous data, symmetry of distribution, and measures of central tendency within a perfectly normal curve.

Empirical Rule
Normal Distribution
Continuous Data
Measures of central tendency
Symmetry of Distribution
Inflection Point
Standard deviation
Mean
Continuous random variable

Measures of Central Tendency

This lecture evaluates the mean, median, and mode as mathematical tools to identify the single score that best represents the center of a data distribution, especially when extreme outliers impact the reliability of the mean.

central tendency
mean
median
mode
midpoint
frequency
bimodal
multimodal
nominal datasets
outliers
skewed datasets
Bi-modal
Multi-modal
Outlier
Data distribution

Variable Types and Levels of Measurement

Definitions and examples of variables, including categorical and numerical types, and the four levels of measurement.

Variable
Categorical Variables
Qualitative Variables
Nominal
Numerical Variables
Quantitative Variables
Discrete
Continuous
Levels of Measurement
Interval Data
Ratio Data
Independent Variables
Dependent Variables

Statistical Analysis and ANOVA Study Guide

A review of key terms including T tests, ANOVA designs, p values, alpha, and parametric versus non-parametric assumptions.

T tests
Paired/dependent vs unpaired/indep
Statistical significance
P value vs alpha
ANOVA design
Repeated measures ANOVA
Post hoc tests
Bonferroni calculation
Subjects vs within subjects factors
Covariate
A priori sample size estimation
Post hoc power analysis
Parametric vs non-parametric assumptions

Exam 1 Key Terms

A study guide covering research design basics, psychometric properties, statistical testing, and quantitative research variables.

Evidence Based Practice
Efficacy vs Effectiveness
Deductive vs Inductive Reasoning
Research Design Basics
Cohort vs Case Control
Single Subject vs Case Study
Prospective vs Retrospective vs Cross Sectional Designs
Reliability vs Validity
Responsiveness (MDC, MCID)
Sensitivity vs Specificity
Likelihood Ratios
Inferential vs Descriptive statistics
Mean/Median/Mode
Normal Distribution and SD
Variables in Quantitative Research
Constructs and operational definitions
Discrete vs Continuous data
Categorical vs Continuous
DV vs IP vs Covariate
Nominal/Ordinal/interval/Ratio

Introduction to A Priori and Post-Hoc Power Analysis

Confidence Interval Analysis in Negative Trials

Using confidence intervals to evaluate negative trials and determine if results exclude a clinically meaningful difference.

confidence interval analysis
negative trials
statistical significance
point estimate
null value
clinically meaningful difference
power analysis
precision of estimate

Fundamentals of Biostatistics Hypothesis Testing

Null and Alternative Hypothesis Testing

Hypothesis testing is the process of determining whether evidence is strong enough to reject the null hypothesis and support the alternative hypothesis.

null hypothesis
alternative hypothesis
research hypothesis
hypothesis testing

Introduction to Biostatistics Hypothesis Testing

This material covers statistical inference through hypothesis testing, including null and alternative hypotheses, p-values, alpha, and confidence intervals.

Statistical inference
Null hypothesis (H0)
Alternative hypothesis (Ha)
Sampling error
Alpha
Type 1 error
P-value
Decision rule
Confidence intervals
Statistical significance
Effect size

Null and Alternative Hypotheses Fundamentals

Hypothesis testing helps decide whether evidence is strong enough to reject the null hypothesis and support the alternative hypothesis.

Null Hypothesis (H0)
Alternative Hypothesis (H1)
Hypothesis testing
Research Interest

One-Tailed and Two-Tailed Hypothesis Testing

The direction specified in the alternative hypothesis determines whether a two-tailed test watches both directions with alpha split or a one-tailed test places all alpha into a single tail to look for a specific direction of change.

one-tailed test
two-tailed test
null hypothesis
alternative hypothesis
alpha level
statistical power
direction of effect
hypothesis testing
Alpha split
Right tail
Left tail
Direction of hypothesis

Evidence-Based Practice, Research Design, and Psychometrics

Evidence Based Practice Pyramid and Research Design

A review of the evidence based pyramid, research design, and the fundamental principles of evidence based practice.

evidence based practice
evidence based pyramid
research design
hierarchy of evidence
control of bias
experimental designs
quasi experimental designs
non experimental designs
patient values and expectations
clinical expertise
systematic reviews
randomised controlled trials
inception cohort studies
efficacy and effectiveness

Review of Psychometric Properties

This lecture reviews psychometric properties including reliability, validity, and responsiveness used by physical therapists to measure patient performance and progress.

reliability
validity
responsiveness
measurement error
intra rater reliability
test retest reliability
ICC
Kappa value
face validity
concurrent validity
predictive validity
minimal detectable change
ordinal scale
floor effect
ceiling effect

Evidence-Based Practice and Research Design

Review of the hierarchy of evidence, research design types, and fundamental principles of evidence-based practice.

Hierarchy of Evidence
Evidence-Based Practice Principles
Systematic Review
Randomized Controlled Trials
Cohort Studies
Case-Control Studies
Case Series
Experimental Research
Quasi-Experimental Designs
Single-subject Design
Deductive Reasoning
Inductive Reasoning
Efficacy vs Effectiveness
Foreground Questions
Univariate and Multivariate Designs
Observational Research

Evidence-Based Practice Sensitivity Specificity and Likelihood Ratios

This material covers sensitivity, specificity, and likelihood ratios used for diagnosis, including SpPins and SnNouts to rule conditions in or out.

Sensitivity
Specificity
Likelihood Ratios
SpPins
SnNouts
True Positive
True Negative
False Positive
False Negative
Pre-test probability
Post-test probability
Positive Likelihood Ratio
Negative Likelihood Ratio

Sensitivity, Specificity, and Likelihood Ratios

Likelihood ratios combine sensitivity and specificity to shift pre-test probability to post-test probability using nomograms to help rule in or rule out a condition.

Likelihood ratios
Sensitivity
Specificity
Pre-test probability
Post-test probability
Positive likelihood ratio
Negative likelihood ratio
Nomograms
Prevalence

EBP II Statistics and Research Fundamentals

This module covers qualitative vs quantitative research, types of statistics, and measures of central tendency.

Qualitative vs Quantitative Research
Inferential Statistics
Descriptive Statistics
p value
Mean difference
Confidence Intervals
Mean
Standard Deviation
Range
Mode
Median
Normal Distribution
Skewness

Variables in Quantitative Research

This content describes types of variables, levels of measure, constructs, and operational definitions used in quantitative research.

Independent variable
Dependent variable
Covariate
Quantitative research
Constructs
Operational definition
Continuous variables
Categorical variables
Level of measure

Psychometric Properties and Diagnostic Test Interpretation

Review terms including psychometric properties, sensitivity, specificity, and likelihood ratios used in diagnostic test studies.

Psychometric properties
Sensitivity
Specificity
Likelihood ratio
Positive likelihood ratio (LR+)
Negative likelihood ratio (LR-)
Pre-test probability
Post-test probability

Fundamentals of Biostatistics, Confidence Intervals, and Power

Introduction to Biostatistics Confidence Intervals

A range of values that we are confident contains the population parameter, including the point estimate and the effect of sample size and variance on precision.

confidence interval
population parameter
point estimate
statistical inference
sampling distribution of means
z-score
standard error of the mean
sample standard deviation
sample size
variance
precision
probability distribution

Introduction to Statistical Power

Statistical power is the probability of finding a statistically significant difference if it exists in the real world.

statistical power
statistical inference
null hypothesis
alternative hypothesis
type I error
type II error
beta
alpha
effect size
variance
sample size

Understanding Statistical Power and Study Design

This content explains how alpha, effect size, variance, and sample size influence statistical power when designing a study.

statistical power
alpha
type I error
effect size
unstandardized effect size
mean difference
variance
standardized effect size
sample size

Understanding Confidence Interval Estimation

A range of values that we are confident contains the population parameter using point estimates and sampling distributions.

Point Estimate
Confidence Interval
Population Parameter
Precision of the estimate
Sampling distribution of means
Standard Error of the Mean (SEM)
Z-score
T-score
Confidence Level

Introduction to Statistical Power

Statistical power is the probability of finding a statistically significant difference if such a difference exists in the real world.

Statistical Power
Statistical Inference
Probability of finding a statistically significant difference

Introduction to Biostatistics Power Analysis

This material covers power analysis including alpha, effect size, sample size, and variance.

Power Analysis
Alpha
Effect Size
Sample Size
Variance
A priori Power Analysis
Post-hoc Power Analysis
Standardized Effect Size
Sample Size Estimation
Statistical Inference

Introduction to Biostatistics and Hypothesis Testing

This lecture focuses on inferential statistics to make inferences about observed differences or relationships in sample data versus population parameters using null and alternative hypotheses.

inferential statistics
population parameters
sample data
sampling error
null hypothesis
alternative hypothesis
population means
sample means
effect size
variance
sample size
type one error
type two error
alpha
beta
hypothesis testing

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