Posted on Categories:Statistical inference, 统计代写, 统计代考, 统计推断

# 统计代写|统计推断代考Statistical Inference代写|Statistical Signifcance as an Indicator of Research Quality

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## 统计代写|统计推断代考Statistical Inference代写|Statistical Signifcance as an Indicator of Research Quality

In recent years, a number of writers (e.g., Atkinson, Furlong, \& Wampold, 1982; Greenwald, 1975; Sterling, 1959; Walster \& Cleary, 1970) have drawn attention to the fact that we treat statistical significance as a desirable object in itself and as an indication of the quality of research. Sterling (1959) and Atkinson et al. (1982) have documented that journals overwhelmingly publish articles reporting statistically significant results. Atkinson et al. submitted a manuscript, less the discussion section, to 50 consulting editors, and found that the same piece of research was rather clearly accepted or rejected according to whether the results were significant or not. ${ }^{13}$ And certainly many graduate students can testify to having been sent back to collect more data for their dissertations or to revise their hypotheses when the results were not significant.
Logically one would think that the merit and informativeness of a piece of research depend on such characteristics as the importance of the question that was asked and the care taken in the design and measures, rather than whether the answer to the question turned out to be yes or no. The rationale for this curious value system was stated by no less portentous an authority than the APA Publication Manual:
Negative results lacking a theoretical context are basically uninterpretable. Even when the theoretical basis for the prediction is clear and defensible, the burden of methodological precision falls heavily on the investigator who reports negative results. .. . Failure to replicate results of a previous investigator, using the same method but a different sample, is generally of questionable value. A single failure may merely testify to sampling error or to the conclusion that one of the two samples had unique characteristics responsible for the reported effect, or the lack of effect. (American Psychological Association [APA], 1974, p. 21)
Atkinson et al. make the obvious but important point that sampling variability is an equally valid explanation for the original, significant result. This passage was deleted from the third edition of the Publication Manual, but the field as a whole has not responded so nimbly.

## 统计代写|统计推断代考Statistical Inference代写|Epistemic Versus Behavioral Orientation

Statements like Guilford’s and Kerlinger’s, quoted above, indicate very clearly their belief -and the sense of the statements is surely close to most psychologists’ understanding-that we need statistical inference fundamentally for epistemic purposes, for the evaluation of hypotheses, and hence, if either the Fisherian or the Neyman-Pearson rationale for significance testing were relevant to psychology, it would clearly be the former. And, indeed, one searches the literature in vain for any argument in favor of the Neyman-Pearson approach, as against the Fisherian, in psychological research. If such arguments were to be found, they would presumably be given in the statistics textbooks; but there, however careful the exposition of the Neyman-Pearson doctrine, the rationale presented seems inevitably to be Fisherian. Kempthorne (1972), in an essay in honor of George Snedecor (who was nearly 90 at the time), notes that the Neyman-Pearson theory is seldom practiced as it is preached, that research workers do not in fact take the decision orientation seriously.
From the viewpoint of the Neyman-Pearson theory of testing hypotheses-or as this author prefers, the Neyman-Pearson theory of accept-reject rules-an inspector is not permitted the following thought process. Suppose two particular data points $D_1$ and $D_2$ fall in the rejection region of size $\alpha=0.05$. Suppose also that $D_1$ falls in the region of size $\alpha=0.01$ and $\mathrm{D}_2$ does not. Then it is very natural to take the view that $\mathrm{D}_1$ disagrees with the null hypothesis more than $\mathrm{D}_2$. But to use phraseology that is becoming current, this would be an evidential conclusion. It appears that no such conclusions are permitted in the Neyman-Pearson theory. Indeed, it can happen that a sample point is in the rejection region of size 0.01 and is not in the rejection region of size 0.05 . It may be true that those who use the Neyman-Pearson theory will reach the evidential conclusion above, and indeed many of the ideas of the theory have been taken over and used in an evidential way. But nothing in the Neyman-Pearson theory permits this activity. (pp. 179-180) I hazard the opinion that Snedecor’s Statistical Methods has had some appeal to scientists and has not been modified in basic outlook by the development of decision theory, because decision theory deals with problems that are so simple (e.g., how to approach the problem of making scrambled eggs) and so simplified as to have no essential relevance to the problems of research and development. (p. 182)

# 统计推断代写

## 统计代写|统计推断代考Statistical Inference代写|Epistemic Versus Behavioral Orientation

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## MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。