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Fundamental Statistical Tools for Research

Fundamental Statistical Tools for Research

An easy intro to key statistical concepts. Learn about data visualization and outlier detection. Demystify statistical significance testing. An excellent introductory module and a solid basis for all other modules.

Course Details

Learning Objectives

Learning Objectives:

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, characterize and identify problems and trends in data using plots
  • Use descriptive statistics to summarize 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
  • Analyze data more quickly and more accurately
  • Interpret results reliably and confidently

Target Audience

Target Audience:

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.

Course Outline

Course Outline:
  • 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

Practical Info

Practical Info:


Recommended Duration: 1 day

Course Materials:

  • Course notes on statistical techniques
  • Sample datasets


    Course Reviews

    • posted by Leslie Lukens

      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!

    • posted by Roger L. Roy

      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.

    • posted by Ian Chapman

      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.

    • posted by Heath Hendershot

      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.

    Related Sessions

    • Classical statistical methods such as ANOVA, linear regression rely on certain data distribution assumptions. Whenever they are not met, alternative methods such as nonparametric may be used. Learn about their principle, advantages and limitations.

    • Efficient experiments must be large enough to detect meaningful scientific differences and maximize the use of available resources. Learn about sample size and power calculations.

    • Variation is present in every experiment. Learn about DOE techniques to control variation, and to maximise data quality. Commonly used experimental designs are discussed as well as the statistical data analysis tools.

    • Learn about key biostatistical concepts and efficient tools for summarising and plotting data as well as outlier detection. Demystify the statistical testing approach used to make decision in the presence of uncertainty: p-values, power, and so on.