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Statistical Comparison of Groups

Several experiments are conducted to determine whether differences exists between procedures, methods, treatments. Learn about the design and the analysis of simple comparative experiments and more complex situations.

 

    This module covers the t-test and the analysis of variance (ANOVA) techniques.
    • Participants must master the essential tools in statistics - descriptive statistics: both numerical (mean, median, standard deviation, standard error, etc.) and graphical (histogram, box-plot, scatter plot, etc.) tools, hypothesis testing and confidence intervals.
    • Future attendees must either have attended the module Introduction to the Design of Experiments DOE or possess an equivalent level.
    • Statistical Tools for Comparing Two Groups
      • Completely Randomized Design / Independent Samples / Parallel Groups
      • Paired / Related Samples
    • Statistical Analysis of Factorial Designs (More Than Two Samples)
      • Estimating the Effect of a Single Factor with Analysis of Variance
      • Estimating the Effect of Two Factors with Analysis of Variance : Unreplicated Case
      • Estimating the Effect of Two Factors with Analysis of Variance : Replicated Case
      • Multi-Way Analysis of Variance
      • Multiple Comparisons Techniques to Locate Group Differences Detected by ANOVA
This session is intended It is also intended for people who work on designed data, survey data, or administrative data. It is also intended for scientists that are comfortable with the design of experiments but need to improve their data analysis of designed experiments skills.
    • This module covers the t-test and the analysis of variance (ANOVA) techniques.
      • Participants must master the essential tools in statistics - descriptive statistics: both numerical (mean, median, standard deviation, standard error, etc.) and graphical (histogram, box-plot, scatter plot, etc.) tools, hypothesis testing and confidence intervals.
Upon completion of this module, participants will be able to:
    • Understand the principle of the t-test
    • Determine when the t-test is appropriate
    • Determine whether the independent or the paired samples t-test should be used
    • Determine whether the underlying assumptions of the t-test are met
    • Choose a remedial measure when assumptions are violated
    • Understand the principle of ANOVA
    • Determine when the ANOVA technique is appropriate to use
    • Check whether the assumptions underlying ANOVA are met
    • Understand the notion of factor interaction, depict and interpret significant ones
    • Read and interpret an ANOVA table
    • Know which post hoc techniques exist along with their pros and cons. How to use them to locate differences detected by ANOVA
    • Analyze data more quickly and more accurately
    • Interpret results reliably and with more confidence
 

Recommended Duration: 2 day(s)

Course Materials:

TBD

Case Studies:

N/A