Built for non-statisticians.
Designed specifically for professionals with little or no statistical background.
Helps you confidently work with statisticians and data teams.
Statistics is a valuable tool that is good and useful for making decisions in the medical research arena. When employed in a field where a p-value can determine the next steps in the development of a drug or procedure, it is authoritative that choice makers comprehend the philosophy and request of statistics.
Quite a few numerical software is now available to professionals. However, this software was industrialized for geometers and can often be unnerving to non-statisticians. How do you know if you are persistent in the right key, let unaided execution be the best test?
And it will profit specialists who must comprehend and work with study design and clarification of findings in a scientific or biotechnology setting.
Stress will be placed on the real numerical (a) concepts, (b) application, and (c) interpretation, and not on mathematical formulas or actual data analysis. A basic understanding of statistics is desired, but not necessary.
Designed specifically for professionals with little or no statistical background.
Helps you confidently work with statisticians and data teams.
What Makes This Training Different?
- Practical, Not Mathematical
- No heavy formulas
- No advanced math required
- Focus on real-world application
By attending, you will be able to:
- Understand key statistical concepts used in clinical trials
- Interpret p-values, confidence intervals, and significance correctly
- Identify appropriate statistical tests for different scenarios
- Evaluate research findings and avoid misleading conclusions
- Understand sample size, bias, and study design fundamentals
- Communicate statistical results clearly within your organization
- The training focuses on concepts, application, and interpretation - not complex formulas
Statistics is a valuable tool that is good and useful for making decisions in the medical research arena. When employed in a field where a p-value can determine the next steps in the development of a drug or procedure, it is authoritative that choice makers comprehend the philosophy and request of statistics.
Quite a few numerical software is now available to professionals. However, this software was industrialized for geometers and can often be unnerving to non-statisticians. How do you know if you are persistent in the right key, let unaided execution be the best test?
And it will profit specialists who must comprehend and work with study design and clarification of findings in a scientific or biotechnology setting.
Stress will be placed on the real numerical (a) concepts, (b) application, and (c) interpretation, and not on mathematical formulas or actual data analysis. A basic understanding of statistics is desired, but not necessary.
Course Content
Agenda Day 1: Basics
Session 1: Why Statistics
Session 2: The many ways of interpretation
Session 3: Types of Data and Descriptive Statistics
Session 4: Common Statistical Tests
Session 1: Logistic Regression
Session 2: Survival Curves and Cox Regression
Session 3: Bayesian Logics
Session 4: Systematic Reviews and Meta-Analysis
Session 1: Other Tests
Session 2: Power and Sample Size
Session 3: How to Review a Journal Article
Session 4: Developing a Statistical Analyis Plan
Agenda
Day 1: Foundations of Statistics (Build Your Core Understanding)Session 1: Why Statistics Matters
- Do we really need statistical tests?
- Sample vs. Population - understanding the difference
- What statistics can and cannot do
- Descriptive statistics & variability explained simply
- Confidence intervals demystified
- Understanding p-values (without confusion)
- Effect sizes and why they matter
- Clinical vs. meaningful significance
- Continuous, Ordinal, and Nominal data
- Normal distribution and why it’s critical
- Graphical data representation
- When and how to transform data
- Comparative statistical tests
- Simple & multiple regression analysis
- Non-parametric techniques
- Live Q&A Session
Session 1: Logistic Regression Made Simple
- When and why to use logistic regression
- Interpreting odds ratios clearly
- Presenting and explaining results
- Working with contingency tables
- Key concepts and terminology
- Kaplan-Meier curves & Log-Rank tests
- Proportional hazards explained
- Interpreting hazard ratios
- Presenting survival analysis results
- A new way to interpret data
- Bayesian vs traditional statistics
- Applications in diagnostic testing
- Use cases in genetics
- Why they are critical in research
- Key terminology and concepts
- Step-by-step systematic review process
- Conducting a meta-analysis
Session 1: Specialized Statistical Tests
- Non-parametric methods
- Equivalency testing
- Non-inferiority testing
- Key theory and calculation steps
- Determining appropriate sample size
- Hands-on demo using G*Power software
- How to critically review journal articles
- Assessing quality and credibility
- Identifying study limitations
- Step-by-step SAP development
- Aligning with regulatory expectations from FDA and MHRA
- Key components of a robust SAP
- Ready-to-use SAP template provided
Course Provider
Elaine Eisenbeisz,
