Discover data visualisation tools, learn the principle underlying hypothesis testing and sample size calculations. Harness the power of design of Experiments “DOE” techniques to control variation and maximise data information.
- The importance of statistics
- Descriptive statistics
- Importance of identifying the type & role of variables
- Visualising and summarising data distributions
- Frequency tables for categorical variables
- Pearson's correlation coefficient for continuous variables
- Plotting Data: Histograms, Scatter, box-plots, bar charts
- What is statistical inference?
- Hypothesis testing principles: Null and alternative hyposhesis, one vs. two-tailed tests
- Risk involved in significance testing
- Test statistics: T-test, F-tests...
- Observed significance level or "p-value"
- Statistical significance & decision rules
- The importance of sample size calculations
- Statistical inference with confidence Intervals
- Numerical application to the single sample case
- Sources of Variation
- Why Design an Experiment?
- Measurement Variability and Error
- The Notion of Experimental Unit
- Controlling and Minimizing Variability: Replication, Randomization, Blocking and Controls
- Integrating Experimental & Budgetary Constraints into the Experimental Design
- Constructing Experimental Designs
- Two-Sample Designs (Complete Randomized Design, Paired Comparison Design)
- Factorial Designs for more than Two Groups (Unreplicated and Replicated)
- Statistical Analysis Tools
- Exploratory Analysis
- Student's T-Test (Independent and Paired T-Test)
- Analysis of Variance (ANOVA) / F-Test
- The Notion of Interactions between Factors
- Locating Statistical Differences with Multiple Comparison Techniques
- Understanding and Interpreting Results from Real Data
This module is aimed at all scientific staff who wish to design and implement efficient studies and experiments and who must make decisions based on the data collected. This module introduces key concepts in statistics and data analysis. It assumes that participants either have no previous knowledge of statistics or that they have not used statistics for a long time.
Upon completion of this module, participants will be able to:
- Understand the difference between descriptive & inferential statistics
- Appreciate the value of exploratory methods in preliminary data analysis & design of experiments
- Explore, characterise and identify problems and trends in data using plots
- Use descriptive statistics to summarise data
- Understand the concepts of hypothesis testing: risks, p-value, confidence intervals, power
- Identify the appropriate statistical test based on the study objective
- Understand the importance of sample size calculations and the required input parameters
- Understand the importance of statistical design of experiments and benefits in R&D
- Learn the experimental designs most widely used in practice
- Choose an appropriate experimental design based on the study objectives
- Construct and implement the design selected
- Analyse the data collected based on the design used and its underlying assumptions
Recommended Duration: 3 day(s)