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The insignificance of statistical significance testing.
 Journal of Wildlife Management,
, 1999
"... Abstract: Despite their wide use in scientific journals such as The Journal of Wildlife Management, statistical hypothesis tests add very little value to the products of research. Indeed, they frequently confuse the interpretation of data. This paper describes how statistical hypothesis tests are o ..."
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Abstract: Despite their wide use in scientific journals such as The Journal of Wildlife Management, statistical hypothesis tests add very little value to the products of research. Indeed, they frequently confuse the interpretation of data. This paper describes how statistical hypothesis tests are often viewed, and then contrasts that interpretation with the correct one. I discuss the arbitrariness of Pvalues, conclusions that the null hypothesis is true, power analysis, and distinctions between statistical and biological significance. Statistical hypothesis testing, in which the null hypothesis about the properties of a population is almost always known a priori to be false, is contrasted with scientific hypothesis testing, which examines a credible null hypothesis about phenomena in nature. More meaningful alternatives are briefly outlined, including estimation and confidence intervals for determining the importance of factors, decision theory for guiding actions in the face of uncertainty, and Bayesian approaches to hypothesis testing and other statistical practices.
Some Aspects of Statistical Significance in Statistics Education Pranesh Kumar
"... Statistical significance in the null hypothesis testing is the primary objective method for representing scientific data as evidence and for measuring strength of that evidence. Statistical significance is measured by calculating the probability value (Pvalue) generated by the null hypothesis test ..."
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Statistical significance in the null hypothesis testing is the primary objective method for representing scientific data as evidence and for measuring strength of that evidence. Statistical significance is measured by calculating the probability value (Pvalue) generated by the null hypothesis test of significance. Several interpretations of Pvalues are possible. For example, Pvalue is interpreted as the probability that the results were obtained due to chance. A small Pvalue would recommend that the null hypothesis is not supported by the sample data and the research hypothesis is strongly favored by data. Alternatively, effect size can be considered as a measure of the extent to which the research hypothesis is true or to the degree to which the findings have practical significance in context of the study population. Effect size measures seem to have advantages over statistical significance because they are not affected by the sample size and are scalefree. The effect size measures can be uniquely interpreted in different studies regardless of the sample size and the original scales of the variables. In this paper we will present some aspects of statistical significance, practical significance and their computations. We will consider statistical significance measures for some commonly used statistical parameters. In conclusion, we present discussions and remarks.