## Statistical Hypothesis Testing

The use of

Statistical Hypothesis Testingprocedure to determinetype Iandtype IIerrors was linked to the measurement ofsensitivity and specificityin clinical trial test and experimental pathogen detection techniques. A theoretical analysis of establishing these types of errors was made and compared to determination ofFalse Positive, False Negative, True Positive and True Negative. Experimental laboratory detection methods used to detect Cryptosporidium spp. were used to highlight the relationship betweenhypothesis testing, sensitivity, specificity and predicted values.Source: Owusu-Ansah et al.

## Medical Research and Binary Classification Test

Sensitivity and specificityare two terms widely used in Medical research and are the statistical measures of performance of abinary classification test. In clinical research the sensitivity of a medical test is the probability of its giving a ‘positive’ result when the patient is indeed positive and specificity is the probability of getting ‘negative’ result when the patient is indeed negative. Wrongly identify a healthy person as sick and a sick person as healthy is closely related to the concept of type I and type II errors of testing hypothesis. It was observed that thesensitivityof a test is equal topowerof test in hypothesis testing.Source: Sharma et al.

## Diagnostic and Statistical Tests

Diagnostic testsguide physicians in assessment of clinical disease states, just asstatistical testsguide scientists in the testing of scientific hypotheses.

Sensitivity and specificityare properties of diagnostic tests and are not predictive of disease in individual patients.Positive and negativepredictive values are predictive of disease in patients and are dependent on both the diagnostic test used and the prevalence of disease in the population

studied. These concepts are best illustrated by study of a two by two table of possible outcomes of testing, which shows that diagnostic tests may lead to correct or erroneous clinical conclusions.In a similar manner,

hypothesis testingmay or may not yield correct conclusions. A two by two table of possible outcomes shows that two types of errors in hypothesis testing are possible.

– One can falsely conclude that a significant difference exists between groups (type I error). The probability of a type I error is a (alpha).

– One can falsely conclude that no difference exists between groups (type II error). The probability of a type II error is b (beta). The consequence and probability of these errors depend on the nature of the research study.

–Statistical powerindicates the ability of a research study to detect a significant difference between populations, when a significant difference truly exists.

–Powerequals 1- b. Because hypothesis testing yields “yes” or “no” answers,confidence intervalscan be calculated to complement the results of hypothesis testing.Finally, just as some abnormal laboratory values can be ignored clinically, some statistical differences may not be relevant clinically.

Source: Gaddis GM et al.

## References

- Statistical tests: which one should you use?
- Baffling Concept of True Positive and True Negative
- Test Statistics
- Sensitivity and specificity
- THE CONCEPT OF SENSITIVITY AND SPECIFICITY IN RELATION TO TWO TYPES OF ERRORS AND ITS APPLICATION IN MEDICAL RESEARCH
- Introduction to biostatistics: Part 3, Sensitivity, specificity, predictive value, and hypothesis testing
- Targeted test evaluation: a framework for designing diagnostic accuracy studies with clear study hypotheses
- Tests for One-Sample Sensitivity and Specificity
- Some Useful Statistics Definitions
- Framing: Key ML Terminology
- ROC Curves
- False Positives and False Negatives – CompTIA Security+ SY0-401: 2.1