If you are looking to learn about statistics, but are reluctant to do so, this is the course for you. It provides an excellent introductory solid basis for all other training courses.
- This course covers the fundamental principles and concepts in statistics
- Classical and more recent exploratory data analysis techniques to efficiently visualise and summarise data are presented.
- The various uses of these methods such as outlier detection will be presented.
- Real-life examples are used to illustrate the principles underlying statistical testing and decision-making in the presence of uncertainty.
- The workshop also covers statistical hypothesis testing, risks involved (alpha & beta), p-values, confidence intervals & statistical significance.
- The principle underlying sample size and power determination will be outlined.
Distance Learning available
- Why Do we Need Statistics?
- Descriptive or Exploratory Data Analysis "EDA"
- Overview and Goals
- Importance of Identifying the Type and Role of Variables in Studies
- Visualizing and Summarizing Data: The Concept of a Distribution
- Graphical Tools: histogram, Box-plot
- Numerical Tools: mean, median, standard deviation, standard error, etc.
- Exploring the relationship between two (2) variables
- Frequency tables for categorical variables
- Pearson's correlation coefficient for continuous variables
- Plots: Scatter plots, box-plots, etc.
- Statistical inference or hypothesis testing
- Overview: What is statistical inference?
- Statistical Inference with Hypothesis Testing:
- Null and alternative hypotheses
- One-tailed vs. two-tailed tests
- Test statistics: t-test, F-tests, etc.
- Observed significance level or "p-value"
- Statistical significance and decision rules
- Risk involved in hypothesis testing
- Risks or type I & II errors
- Confidence level of a test
- Power of test
- The importance of sample size calculations and the required input parameters to estimate a sample size
- Statistical inference with confidence Intervals: interpretation and usage
- Statistical Inference for a Single Sample or Group: Hypothesis Testing vs. Confidence Interval Approach
- To distinguish descriptive from inferential statistics
- To appreciate the value of exploratory data analysis - EDA methods in preliminary data analysis & in experimental design construction
- To explore, characterise & identify problems and trends in data using data visualisation tools
- To understand the principles underlying statistical hypothesis testing, confidence intervals, p-values, risk & power
- To understand the importance of sample size & power calculations and required input parameters
- To perform simple data analyses more quickly and accurately
- To interpret results reliably & with confidence
Recommended Duration: 1 day(s)