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Data Analysis for Internal Auditors

Course Description

Print-friendly Course Description and Outline

Are you seeking to improve the effectiveness of your audit planning and to ensure whether or not your results can be generalized to total populations? This one-day course provides the opportunity to learn about the analysis of large data sets, particularly how to summarize data, display data, and determine the appropriate measures for describing data. The course includes determining the types of evidence needed and the various evidence collection methods.

In order to have a cost-effective and value-added audit, you need to make maximum use of the tools available to the auditor in planning. If you don’t understand population analytics and sample analytics, then you run the risk of moving into conduct too soon without the appropriate preparation for evidence collection. Consequently, either crucial data may not be collected or may be collected in such a way that it cannot be generalized to the population. In either case, there would be an increase in cost and loss of value.

Specifically, participants will learn how to:

  • Appropriately use population analytics (analysis of shape, central tendency, variation, correlation, and linear regression).
  • Effectively use benchmarking during planning to identify areas that may have potentially weak controls.
  • Correctly define of the boundaries of data that constitute various types of evidence.
  • Correctly generalize (or not) findings to total populations of data.

This course applies to all auditors at all experience levels in both the public and private sectors.

Course Duration: 1 day(s)
CPE Hours Available: 8
Knowledge Level: Intermediate
Field of Study: Statistics
Statistical Sampling for Internal Auditors provides an excellent foundation for this course, although it is not required that you take it first.
Advance Preparation: 
Delivery Format: eLearning (Group-Internet-Based); On-site Training (Group-Live)

​Introduction to Data Analysis

  • Summarize introductory terminology and methodology related to data analysis
  • Determine when population analytics and sample analytics occur in the planning phase of an audit
  • Explain why a sample must have the same characteristics as the population
  • Distinguish between grouping variables and dependent variables
  • Distinguish between discrete and continuous variables

Population Analytics: Shapes of Frequency Distributions

  • Evaluate shapes of distribution relevant to their important characteristics
  • Recognize uniform, skewed, bimodal, and normal shapes of distribution
  • Determine the important characteristics of uniform, skewed, bimodal, and normal shapes of distribution

Population Analytics: Measures of Central Tendency and Variation

  • Determine which measure of central tendency and variation to use based on the frequency distribution
  • Calculate the measures of central tendency
  • Evaluate whether the assumption for each measure of tendency and variation have been met

Population Analytics: Comparison to Benchmarks

  • Evaluate the important differences between a set of data and a chosen benchmark standard using shape, central tendency, and variation
  • Discuss the reason for benchmarking
  • List possible sources of benchmarks

Population Analytics: Correlations and Linear Regression

  • Determine when to use correlation and linear regression
  • Define correlations
  • Interpret correlations
  • Define linear regression
  • Draw a linear regression line
  • Identify a situation for the use of correlations and for the use of linear regression

Sample Analytics: Types of Evidence

  • Determine the type of evidence needed to evaluate the effectiveness of controls and their appropriate evidence collection methods
  • Define sample analytics
  • Discuss sub-dividing populations
  • List types of evidence
  • Identify the boundaries for different evidence collection methods

Sample Analytics: Random and Judgmental Sampling

  • Discuss the differences among census, judgmental, and random sampling
  • Define census, judgmental and random sampling
  • Discuss random and judgmental sample selection
  • Discuss the advantages and disadvantages of probability statements

Sample Analytics: How to Create Questionnaires

  • Create a questionnaire
  • Identify types of statements
  • Create statements that form the basis of a questionnaire
  • Create likert scales
  • Discuss the amount of demographic information needed and the sample size

Sample Analytics:  Evidence Collection Methods

  • Identify evidence collection methods and tools as well as data collection that can be performed with allotted resources
  • Give examples of types of evidence, evidence collection tools, demographic information, and data details for each evidence collection method
  • Identify how to adjust data analysis to align with allotted resources

​Most courses can be delivered through on-site training. You might be surprised that the organization leading the profession is just as committed to the delivery of affordable training.

Contact us by calling +1-407-937-1388 or send an e-mail to

Details and pricing
November 16-17,

Details and pricing
December 4-7,