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StatsToDo : Prediction Statistics Explained

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Related Links:
Prediction Statistics Program Page
Sample Size for Prediction Statistics Explained and Tables Page
Sample Size for Prediction Statistics Program Page

Introduction Terminology and Formula Examples References
In StatsToDo, the discussions and algorithm of Prediction Statistics concerns the quality of relationship between a binary Test and a binary Outcome.

In reality, a much wider domain of tests and outcomes exists. Outcomes that are measurements, such as birth weight, and containing multiple categories, such as mental illness classifications, require specific and complex multivariate methods of analysis, and are covered elsewhere. Tests that are measurements, such as maternal height and age, requires different treatment and are considered in the Receiver Operator Characteristics (ROC) Explained Page .

Outcome PositiveOutcome Negative
Test PositiveTrue PositiveFalse Positive
Test NegativeFalse NegativeTrue Negative

The framework under consideration for this page, in the Prediction Statistics Program Page , and in the Sample Size for Prediction Statistics Explained and Tables Page , is therefore as presented in the table to the right.

  • True Positive (TP) if they are test positive and outcome positive
  • False Positive (FP) if they are test positive but outcome negative
  • False Negative (FN) if they are test negative but outcome positive
  • True Negative (TN) if they are test negative and outcome negative
The statistics of evaluating the relationship between tests and outcomes occurs under the following scenarios
  1. The collection of reference data, to evaluate the quality of relationship between a test and an outcome.
  2. Developing the parameters that can be generalized and use in clinical situation in the future
  3. In appropriate future situations, the use of the parameter to influence diagnostic decisions.
The terminologies and formulae for these procedures will be covered in the next panel

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