P Value and Statistical Significance
Journal Title: Current Trends on Biostatistics & Biometrics - Year 2018, Vol 1, Issue 1
Abstract
The practice of reporting P-values is commonplace in applied research. Presenting the result of a test only as the rejection or acceptance of the null hypothesis at a certain level of significance, does not make full use of the information available from the observed value of the test statistic. Rather P-values have been used in the place of hypothesis tests as a means of giving more information about the relationship between the data and the hypothesis. In this brief note we discuss how to obtain P-values with R codes. Very often in practice we are called upon to make decisions about populations on the basis of sample data. In attempting to reach decisions, it is useful to make assumptions or guesses about the populations involved. Such assumptions which may or may not be true, are called statistical hypotheses and in general are statements about the population parameters. The entire procedure of testing of hypothesis that consists of setting up what is called a ’Null hypothesis’ and testing it. R.A. Fisher quotes, ’Every experiment may be said to exist only in order to give the facts about a chance of disproving the null hypothesis’. So, what is this null hypothesis?”. For example, if we consider the measurements on weights of newborn babies, then the observations on these measurements follows Normal distribution is a null hypothesis [1]. Suppose the measurements denoted by a random variable X that is thought to have a normal distribution with mean μ and variance 1, denoted by N(μ,1). The usual types of hypotheses concerning mean μ in which one is interested include H0: μ = μ0 versus H1: μ6= μ0(two-tailed hypothesis) and H0: μ ≤ μ0 versus H1: μ > μ0 and H0: μ ≥ μ0 versus H1: μ < μ0(one-tailed hypothesis). So null hypothesis H0 is a hypothesis which is tested for possible rejection under the assumption that it is true.
Authors and Affiliations
Vidya Laxmi K
Keywords
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- EP ID EP640195
- DOI 10.32474/CTBB.2018.01.000102
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