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# 统计代写| 假设检验作业代写Hypothesis testing代考|Only Need to Detect Effects in One Direction

##### 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|Only Need to Detect Effects in One Direction

In this scenario, effects can exist in both directions, but you only care about detecting an effect in one direction. Analysts use the one-tailed approach in this situation to boost the statistical power of the hypothesis test.

To even consider using a one-tailed test for this reason, you must be entirely sure there is no need to detect an effect in the other direction. While you gain more statistical power in one direction, the test has absolutely no power in the other direction.

Suppose you are testing a new vaccine and want to determine whether it’s better than the current vaccine. You use a one-tailed test to improve the test’s ability to learn whether the new vaccine is better. However, that’s unethical because the test cannot determine whether it is less effective. You risk missing valuable information by testing in only one direction.

However, there might be occasions where you, or science, genuinely don’t need to detect an effect in the untested direction. For example, suppose you are considering a new part that is cheaper than the current part. Your primary motivation for switching is the price reduction. The new part doesn’t have to be better than the existing part, but it cannot be worse. In this case, it might be appropriate to perform a one-tailed test that determines whether the new part is worse than the old part. You won’t know if it is better, but you don’t need to know that.

As I mentioned, many statisticians don’t think you should use a onetailed test for this type of scenario. My position is that you should set up a two-tailed test that produces the same power benefits as a onetailed test because that approach will accurately capture the underlying fact that effects can occur in both directions.

However, before explaining this alternate approach, I need to describe an additional problem with the above scenario.

## 统计代写|假设检验作业代写HYPOTHESIS TESTING代考|Alternative: Two-Tails with a Higher Alpha

In my view, determining the possible directions of an effect and the statistical power of the analysis are two independent issues. Using a one-tailed test to boost power can obscure these matters and their ramifications. My recommendation is to use the following process:

1. Identify the directions that an effect can occur, and then choose a one-tailed or two-tailed test accordingly.
2. Choose the significance level to correctly set the sensitivity and false-positive rate based on your specific requirements.
This process breaks down the questions you need to answer into two separate issues, which allows you to consider each more carefully.
Now, let’s apply this process to the scenario where you’re studying an effect that can occur in both directions, but the following are both true:
• You care about effects in only one direction.
• Increasing the power of the test is worth a higher risk of false positives in that direction.

In this situation, using a one-tailed test to gain extra power seems like an acceptable solution. However, that approach attempts to solve the

right problem by using the wrong methodology. Here’s my alternative method.

Instead of using a one-tailed test, consider using a two-tailed test and doubling the significance level, such as from $0.05$ to $0.10$. This approach increases your power while allowing the test methodology to match the reality of the situation better. It also increases the transparency of your goals as the analyst.

To understand this approach, compare the graphs below. The top chart is one-sided and uses a significance level of $0.05$ while the bottom plot is two-sided and uses a significance level of $0.10$.

## 统计代写|假设检验作业代写HYPOTHESIS TESTING代考|ALTERNATIVE: TWO-TAILS WITH A HIGHER ALPHA

1. 确定影响可能发生的方向，然后相应地选择单尾或双尾测试。
2. 根据您的具体要求选择显着性水平以正确设置灵敏度和误报率。
此过程将您需要回答的问题分解为两个单独的问题，这样您就可以更仔细地考虑每个问题。
现在，让我们将此过程应用于您正在研究可能在两个方向上发生的效果的场景，但以下都是正确的：
• 你只关心一个方向的效果。
• 增加测试的力量值得在那个方向增加误报的风险。

## 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。