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# 统计代写| 假设检验作业代写Hypothesis testing代考|Interpreting Failures to Reject the Null

##### 空白假设的早期选择

Paul Meehl认为，无效假设的选择在认识论上的重要性基本上没有得到承认。当无效假设是由理论预测的，一个更精确的实验将是对基础理论的更严格的检验。当无效假设默认为 “无差异 “或 “无影响 “时，一个更精确的实验是对促使进行实验的理论的一个较不严厉的检验。

1778年：皮埃尔-拉普拉斯比较了欧洲多个城市的男孩和女孩的出生率。他说 “很自然地得出结论，这些可能性几乎处于相同的比例”。因此，拉普拉斯的无效假设是，鉴于 “传统智慧”，男孩和女孩的出生率应该是相等的 。

1900: 卡尔-皮尔逊开发了卡方检验，以确定 “给定形式的频率曲线是否能有效地描述从特定人群中抽取的样本”。因此，无效假设是，一个群体是由理论预测的某种分布来描述的。他以韦尔登掷骰子数据中5和6的数量为例 。

1904: 卡尔-皮尔逊提出了 “或然性 “的概念，以确定结果是否独立于某个特定的分类因素。这里的无效假设是默认两件事情是不相关的（例如，疤痕的形成和天花的死亡率）。[16] 这种情况下的无效假设不再是理论或传统智慧的预测，而是导致费雪和其他人否定使用 “反概率 “的冷漠原则。

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## 统计代写| 假设检验作业代写Hypothesis testing代考|Interpreting Failures to Reject the Null

Accepting the null hypothesis would indicate that you’vi prorun an effect doesn’t exist. As you’ve seen, that’s not the case at all. You can’t prove a negative! Instead, the strength of your evidence falls short of being able to reject the null. Consequently, we fail to reject it.

Failing to reject the null indicates that our sample did not provide sufficient evidence to conclude that the effect exists. However, at the same time, that lack of evidence doesn’t prove that the effect does not exist. Capturing all that information leads to the convoluted wording! What are the possible interpretations of failing to reject the null hypothesis? Let’s work through them.

First, it is possible that the effect indeed doesn’t exist in the population, so your hypothesis test didn’t detect it in the sample. Makes sense, right? While that is one possibility, it doesn’t end there.

Another possibility is that the effect exists in the population, but the test didn’t detect it for a variety of reasons. These reasons include the following:

• The sample size was too small to detect the effect.
• The variability in the data was too high. The effect exists, but the noise in your data swamped the signal (effect).
• By chance, you collected a fluky sample. When dealing with random samples, chance always plays a role in the results. The luck of the draw might have caused your sample not to reflect an effect that exists in the population.

Notice how studies that collect a small amount of data or low-quality data are likely to miss an effect that exists? These studies had an inadequate ability to detect the effect. We certainly don’t want to take results from low-quality studies as proof that something doesn’t exist!
However, failing to detect an effect does not necessarily mean a study is low-quality. Random chance in the sampling process can work against even the best research projects!

## 统计代写| 假设检验作业代写HYPOTHESIS TESTING代考|Using Confidence Intervals to Compare Means

We’re shifting gears to close this chapter. The previous sections showed you different ways to compare means using t-tests. This

section shows you one way NOT to compare two means that I’ve seen people use too often.

Analysts often compare the confidence intervals for two groups to determine whether the difference between two means is statistically significant. If those intervals overlap, they conclude that the difference between groups is not statistically significant. If there is no overlap, the difference is significant.

While this visual method of assessing the overlap is easy to perform, it regrettably reduces your ability to detect differences. Fortunately, there is a simple solution to this problem that allows you to perform a simple visual assessment and yet not diminish the power of your analysis.

I’ll start by showing you the problem in action and explain why it happens. Then, we’ll proceed to an easy alternative method that avoids this problem.

Determining whether confidence intervals overlap is an overly conservative approach for identifying significant differences between groups. It’s true that when confidence intervals don’t overlap, the difference between groups is statistically significant. However, when there is some overlap, the difference might still be significant.

Suppose you’re comparing the mean strength of products from two groups and graph the $95 \%$ confidence intervals for the group means, as shown below.

## 统计代写| 假设检验作业代写HYPOTHESIS TESTING代考|INTERPRETING FAILURES TO REJECT THE NULL

• 样本量太小，无法检测到效果。
• 数据的可变性太高了。效果存在，但数据中的噪声淹没了信号和FF和C吨.
• 一个偶然的机会，你收集了一个侥幸的样本。在处理随机样本时，机会总是在结果中起作用。抽签的运气可能导致您的样本无法反映总体中存在的影响。

## 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。