Inferential Analysis

Inferential Analysis Interpret Draw Conclusions 

Inferential statistical analysis involves objectively and quantitatively summarizing the data, determining which data patterns are significant, and making inferential statements about system performance.  Inferential statistics provide the tools necessary to identify critical factors and to what degree specific test results can be generalized to the system as a whole.  Here, you can learn about the statistical foundations that allow evaluators to make more broad and stronger claims about systems than allowed by descriptive and exploratory analyses.

EDA sub head
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Checkbox_nobackgroundStatistically Model
& Test
Fit statistical models to data and test significance of data patterns.- Hypothesis Tests
- Inference
- Effect Sizes
- Uncertainty
- Model Selection & Fit
Checkbox_nobackgroundVisualize & Report ResultsCommunicate findings and conclusions to decision makers.- Model Visualization
- Summary Tables
- Practical Interpretation
- Case Studies

Core Concepts & Benefits of Statistical Modeling

Statistical modeling summarizes the results of a test and presents them in such a way that humans can more easily see and understand patterns within the data.  Without statistical modeling, the test team is often left only with impressions of how well a system performed, or gut-feelings of whether one system is better than another.

In this section you will learn how statistical modeling allows us to talk about results, including the magnitude of differences, strength and types of relationships, and the degree of confidence we can have in results.

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Basic Statistical Analyses

Whereas test and evaluation often employs advanced statistical techniques, many are extensions of basic analyses.  This section outlines general statistical techniques that could be appropriate to analyze the results of simple tests and that lay the foundation for many of the techniques highlighted in the system-analysis case study section.

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