Useful Links
Biology
General Biology
Directed Research in Biology
1. Foundations of Scientific Inquiry
2. Designing the Research Project
3. Conducting the Research
4. Data Analysis and Interpretation
5. Scientific Communication
6. Professional Development and Future Steps
Data Analysis and Interpretation
Preparing Data for Analysis
Data Cleaning and Formatting
Removing Outliers
Outlier Detection Methods
Outlier Treatment Options
Documentation Requirements
Standardizing Data Formats
Consistent Units
Decimal Places
Missing Value Codes
Handling Missing Data
Imputation Methods
Mean Imputation
Regression Imputation
Multiple Imputation
Reporting Missing Data
Missing Data Patterns
Impact Assessment
Transparency Requirements
Organizing Data in Spreadsheets
Structuring Data Tables
Row and Column Organization
Variable Naming
Data Entry Standards
Using Formulas and Functions
Basic Calculations
Statistical Functions
Data Validation
Descriptive Statistics
Measures of Central Tendency
Mean
Arithmetic Mean
Geometric Mean
Harmonic Mean
Median
Calculation Methods
Robustness Properties
Appropriate Applications
Mode
Unimodal Distributions
Multimodal Distributions
Categorical Data
Measures of Dispersion
Range
Simple Range
Interquartile Range
Percentile Ranges
Variance
Population Variance
Sample Variance
Interpretation
Standard Deviation
Calculation Methods
Interpretation
Coefficient of Variation
Standard Error of the Mean
Calculation
Confidence Intervals
Sample Size Effects
Data Distribution and Normality
Normal Distribution Properties
Normality Testing
Non-Normal Distributions
Inferential Statistics
Understanding Statistical Significance
Type I and Type II Errors
Alpha Levels
Statistical Power
P-values and Confidence Intervals
P-value Interpretation
Confidence Interval Construction
Relationship Between P-values and Confidence Intervals
Choosing the Appropriate Statistical Test
Parametric vs Nonparametric Tests
Assumptions Comparison
Power Considerations
Robustness Properties
Assumptions of Statistical Tests
Normality Assumptions
Independence Assumptions
Homogeneity of Variance
Common Statistical Tests in Biology
T-tests
Student's T-test
One-Sample T-test
Two-Sample T-test
Assumptions and Limitations
Paired T-test
Before-After Comparisons
Matched Pairs Design
Assumption Checking
Analysis of Variance (ANOVA)
One-way ANOVA
Between-Group Comparisons
Post-hoc Tests
Effect Size Measures
Two-way ANOVA
Main Effects
Interaction Effects
Factorial Designs
Chi-Square Test
Goodness of Fit Test
Test of Independence
Expected Frequency Requirements
Correlation Analysis
Pearson Correlation
Linear Relationships
Assumptions
Interpretation
Spearman Correlation
Rank-Based Correlation
Non-Linear Relationships
Robustness Properties
Linear Regression
Simple Linear Regression
Model Fitting
Assumption Checking
Prediction
Multiple Regression
Multiple Predictors
Model Selection
Multicollinearity
Data Visualization
Principles of Effective Graphing
Clarity and Simplicity
Visual Clarity
Avoiding Clutter
Appropriate Scaling
Labeling Axes and Legends
Descriptive Labels
Units of Measurement
Legend Placement
Creating Figures and Tables
Formatting for Publication
Journal Requirements
Resolution Standards
File Formats
Caption Writing
Descriptive Captions
Statistical Information
Abbreviation Definitions
Common Graph Types
Bar Charts
Categorical Data Display
Error Bar Inclusion
Comparison Facilitation
Line Graphs
Continuous Data Display
Trend Visualization
Multiple Series
Scatter Plots
Relationship Visualization
Correlation Display
Outlier Identification
Box Plots
Distribution Summary
Outlier Display
Group Comparisons
Histograms
Frequency Distributions
Bin Selection
Normality Assessment
Using Statistical Software for Visualization
Microsoft Excel
Basic Graphing
Chart Customization
Data Analysis Tools
R
ggplot2 Package
Base Graphics
Statistical Plotting
Python (Matplotlib, Seaborn)
Matplotlib Basics
Seaborn Statistical Plots
Customization Options
GraphPad Prism
Scientific Graphing
Statistical Analysis
Publication Quality
Interpreting Results
Relating Statistical Findings to Biological Hypotheses
Statistical vs Biological Significance
Effect Size Interpretation
Practical Implications
Distinguishing Statistical vs Biological Significance
P-value Limitations
Effect Size Importance
Clinical Significance
Acknowledging Study Limitations
Sources of Error
Systematic Errors
Random Errors
Measurement Errors
Generalizability
Population Representativeness
External Validity
Scope of Conclusions
Formulating Conclusions
Evidence-Based Conclusions
Hypothesis Support
Alternative Explanations
Suggesting Future Research Directions
Unanswered Questions
Methodological Improvements
Extended Applications
Previous
3. Conducting the Research
Go to top
Next
5. Scientific Communication