Week-8-Common-Statistical-Techniques

Week 8: Common Statistical Techniques

Objectives for Today

• Check-in

• Lecture

• Break

• Class activity

• Reconvene to go over class activity

Introduction to Common Statistical Tests

• Focus on describing common tests

• Preparation for next week's topic: interpretation of these tests

Considerations for Statistical Tests

• Number of groups being compared

• Groups: related (paired) or independent

• Size of sample

• Distribution of data

Parametric vs Non-parametric Tests

• Parametric Tests:

  • Assume data follows a specific distribution (usually normal)

  • Sensitive to underlying distribution and sample size

• Non-parametric Tests:

  • Fewer assumptions about data distribution

  • Used for non-normally distributed data, ordinal data, or when parametric conditions aren’t met

• Key Differences:

  • Parametric: Assumptions about distribution; sensitive to outliers

  • Non-parametric: Robust against outliers; also known as "distribution free"

When to Use Parametric Tests

• When data meets assumptions: normality and sample size

• Large sample size: >25

• Continuous outcome variables

When to Use Non-parametric Tests

• Small sample size: <25

• Heavily skewed data

• Categorical outcome data

Independent vs. Dependent Samples

• Independent Samples:

  • Unrelated groups (e.g., comparing blood pressure in nurses vs doctors)

• Dependent Samples:

  • Related groups (e.g., blood pressure before and after drug treatment in the same patients)

Common Statistical Tests

• Chi-square

• T-test

• Correlation

• Regression

Chi-square Test

• Null Hypothesis (Ho): No relationship between categorical variables

• Alternative Hypothesis (Ha): Relationship exists

• Non-parametric:

  • Independent samples

  • Outcome: nominal or ordinal

• McNemar test for paired data

Chi-square Methodology

• Based on counts, not standard deviation

• Compares expected vs. observed frequencies

• Does not measure strength of association

Examples for Chi-square Use

• HPV vaccination status and cervical cancer relationship

• Lung cancer and smoking status correlation

Correlation Tests

• Ho: No correlation between two variables

• Ha: There is a correlation

• Focus on the strength and direction of the relationship

• Pearson's r: Measures linear relationships

• Spearman correlation: Non-parametric equivalent

Examples for Correlation Use

• Correlation between height and age

• Steps per day and age at mortality

T-test (Independent)

• Ho: No significant difference between means of two groups

• Ha: Significant difference exists

• Binary independent variable with continuous outcome

• Mann-Whitney U test: Non-parametric equivalent

Examples of T-test Use

• Comparing V02 max in intervention groups

• Audiogram scores vs. noise exposure

ANOVA (One-way)

• Ho: No significant difference between means of three or more groups

• Ha: Significant difference exists

• Categorical independent variable with continuous outcome

• Kruskal-Wallis test: Non-parametric equivalent

Examples for ANOVA Use

• Fall risks among different age groups

• Patient readmission rates between hospitals

Paired T-test

• Ho: No difference between means of paired data

• Ha: Significant difference exists

• Compares data from same subjects under different conditions

• Wilcoxon signed-rank test: Non-parametric equivalent

Examples for Paired T-test Use

• Knowledge scores before and after an education intervention

• Mean A1c levels before and after diabetes intervention

Importance of Understanding Statistical Tests

• Helps interpret results and understand research methodologies

• Basic knowledge is crucial for analyzing studies

Reminders for Next Week

• Questions or concerns?

• Assignment 1 due November 8, 5 PM: Group work required; one submission needed per group, all submit quiz portion.