Understanding and Applications of Test Characteristics and Basic Inferential Statistics in Hypothesis Testing (original) (raw)
Scientific researches and diagnostics tools in medical and applied sciences have an important role to play in the health care system as well as agricultural production and development of a nation. In view of radical change in the research spectrum, the scenario is becoming difficult and interesting for the researchers and associated scholars. The statisticians design the experiment, analyze the data and interpret the facts with the help of traditional statistical techniques and statistical inference helps to draw the conclusions in scientific manner. Characteristic for diagnostic test provides the idea to physician in true assessment of clinical disease and statistical inference provides the idea or guide to scientists in the testing of research hypothesis and their interpretation. Sensitivity, specificity, positive predictive value and negative predictive value are collectively known as test characteristics. It is more important ways to express the usefulness of diagnostic tests. It is also more important to understanding of sensitivity, specificity, positive predictive value, negative predictive value and their significant applications and interpretation in applied sciences. Generally, test characteristics guide the clinician in assessment of disease entities. In a similar manner statistical inference guide the researcher in the testing of research hypothesis and interpretation. It is necessary to understand the basics of test characteristics and hypothesis testing to gain appreciation. These test characteristics and statistical inferences are more useful in medical and agricultural sciences (animal science, plant pathology, etc.). In this article, we discussed the basic understanding to calculate sensitivity, specificity, positive predictive value and negative predictive value and their significant interpretation and also discussed the basic statistical inferential techniques. We have discussed the importance of these measures and provided how we should use these measures in our day-today applied research.
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