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The case against statistical significance testing
 Harvard Educational Review
, 1978
"... In recent years the use of traditional statistical methods in educational research has increasingly come under attack. In this article, Ronald P Carver exposes the fantasies often entertained by researchers about the meaning of statistical significance. The author recommends abandoning all statistic ..."
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In recent years the use of traditional statistical methods in educational research has increasingly come under attack. In this article, Ronald P Carver exposes the fantasies often entertained by researchers about the meaning of statistical significance. The author recommends abandoning all statistical significance testing and suggests other ways of evaluating research results. Carver concludes that we should return to the scientific method of examining data and replicating results rather than relying on statistical significance testing to provide equivalent information. Statistical significance testing has involved more fantasy than fact. The emphasis on statistical significance over scientific significance in educational research represents a corrupt form of the scientific method. Educational research would be better off if it stopped testing its results for statistical significance. The case against statistical significance testing has been developed by many critics (see Morrison & Henkel, 1970b). For example, after a detailed analysis Bakan (1966) concluded that &quot;the test of statistical significance in psychological research may be taken as an instance of a kind of essential mindlessness in the conduct of research &quot; (p. 436); and as early as 1963
Null Hypothesis Significance Testing: A Review of an Old and Continuing Controversy
 Psychological Methods
, 2000
"... Null hypothesis significance testing (NHST) is arguably the mosl widely used approach to hypothesis evaluation among behavioral and social scientists. It is also very controversial. A major concern expressed by critics is that such testing is misunderstood by many of those who use it. Several other ..."
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Null hypothesis significance testing (NHST) is arguably the mosl widely used approach to hypothesis evaluation among behavioral and social scientists. It is also very controversial. A major concern expressed by critics is that such testing is misunderstood by many of those who use it. Several other objections to its use have also been raised. In this article the author reviews and comments on the claimed misunderstandings as well as on other criticisms of the approach, and he notes arguments that have been advanced in support of NHST. Alternatives and supplements to NHST are considered, as are several related recommendations regarding the interpretation of experimental data. The concluding opinion is that NHST is easily misunderstood and misused but that when applied with good judgment it can be an effective aid to the interpretation of experimental data. Null hypothesis statistical testing (NHST1) is arguably the most widely used method of analysis of data collected in psychological experiments and has been so for about 70 years. One might think that a method that had been embraced by an entire research community would be well understood and noncontroversial after many decades of constant use. However, NHST is very controversial.2 Criticism of the method, which essentially began with the introduction of the technique (Pearce, 1992), has waxed and waned over the years; it has been intense in the recent past. Apparently, controversy regarding the idea of NHST more generally extends back more than two and a half
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.
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"... About some misconceptions and the discontent with statistical tests in psychology 1 ..."
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About some misconceptions and the discontent with statistical tests in psychology 1
FACTORS AFFECTING TALENT DEVELOPMENT: DIFFERENCES IN GRADUATE STUDENTS ACROSS DOMAINS By
, 2012
"... ii THE GRADUATE COLLEGE We recommend the dissertation prepared under our supervision by ..."
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ii THE GRADUATE COLLEGE We recommend the dissertation prepared under our supervision by
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.