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Fundamentals Statistical Tools for Research

Fundamentals Statistical Tools for Research

This course covers the fundamental principles and concepts in statistics as well as classical and more recent exploratory data analysis (EDA) techniques to describe data with numerical and graphical tools. The various uses of these methods like outlier detection will be presented. With the help of real-life examples the principles underlying statistical testing and decision-making in the presence of uncertainty will be reviewed. The workshop will also cover risks involved (alpha and beta), p-values and statistical significance. The use and interpretation of confidence intervals will also be discussed. This course is an excellent introductory solid basis for all other training courses.  

  • 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
  • Summary
 
This module is aimed at anyone who works with data and who must make decisions based on them.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 «DOE»
  • 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
  • Analyse data more quickly and more accurately
  • Interpret results reliably and confidently
 

Recommended Duration: 1 day(s)

Course Materials:

TBD

Case Studies:

N/A

This Session Has 4 Reviews

  1. Excellent overview of statistics. I feel like I was able to obtain a strong understanding of the basics and will now be able to build on that. Highly recommended!

  2. Excellent overview of the most important concepts in statistics. The box-plot graphical tool was excellent in helping to determine if data can be considered independent, normally distributed samples so that standard statistical analysis can be conducted. The scope of statistical tests, use of the p-value, and how to minimize risks were all clearly explained. Finally, how to interpret confidence intervals and the equivalence between confidence intervals and hypothesis testing helped me to gain more confidence in my analyses.

  3. I started with a minimal knowledge of statistics and statistical methods and tests. This course brought me right up to speed with my colleagues. The concepts were clearly explained and there were several examples of each concept. Overall, an enjoyable and informative course.

  4. Over-all the course was great. The teacher was very thorough in all aspects of the class. The class was well taught, the teacher asked questions to make sure we all understood, got class participation and incorporated in real examples from each participant.

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