19th Ave New York, NY 95822, USA

统计代写| 假设检验作业代写Hypothesis testing代考|The Good Side of High P-values

如果你也在 怎样代写假设检验Hypothesis这个学科遇到相关的难题,请随时右上角联系我们的24/7代写客服。假设检验Hypothesis是假设检验是统计学中的一种行为,分析者据此检验有关人口参数的假设。分析师采用的方法取决于所用数据的性质和分析的原因。假设检验是通过使用样本数据来评估假设的合理性。

统计假设检验是一种统计推断方法,用于决定手头的数据是否充分支持某一特定假设。

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。

my-assignmentexpert™ 假设检验Hypothesis作业代写,免费提交作业要求, 满意后付款,成绩80\%以下全额退款,安全省心无顾虑。专业硕 博写手团队,所有订单可靠准时,保证 100% 原创。my-assignmentexpert™, 最高质量的假设检验Hypothesis作业代写,服务覆盖北美、欧洲、澳洲等 国家。 在代写价格方面,考虑到同学们的经济条件,在保障代写质量的前提下,我们为客户提供最合理的价格。 由于统计Statistics作业种类很多,同时其中的大部分作业在字数上都没有具体要求,因此假设检验Hypothesis作业代写的价格不固定。通常在经济学专家查看完作业要求之后会给出报价。作业难度和截止日期对价格也有很大的影响。

想知道您作业确定的价格吗? 免费下单以相关学科的专家能了解具体的要求之后在1-3个小时就提出价格。专家的 报价比上列的价格能便宜好几倍。

my-assignmentexpert™ 为您的留学生涯保驾护航 在假设检验Hypothesis作业代写方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在假设检验Hypothesis代写方面经验极为丰富,各种假设检验HypothesisProcess相关的作业也就用不着 说。

我们提供的假设检验Hypothesis及其相关学科的代写,服务范围广, 其中包括但不限于:

  • 时间序列分析Time-Series Analysis
  • 马尔科夫过程 Markov process
  • 随机最优控制stochastic optimal control
  • 粒子滤波 Particle Filter
  • 采样理论 sampling theory
统计代写| 假设检验作业代写Hypothesis testing代考|The Good Side of High P-values

统计代写| 假设检验作业代写Hypothesis testing代考|The Good Side of High P-values

Can high p-values be helpful? What do high p-values mean?
Typically, when you perform a hypothesis test, you want to obtain low p-values that are statistically significant. Small p-values are sexy. They represent exciting findings and can help you get articles published.
However, you might be surprised to learn that higher p-values, which are not statistically significant, are also valuable. In this section, I’ll show you the potential value of a p-value greater than $0.05$, or whatever significance level you’re using.

Hypothesis testing is a form of inferential statistics. You want to use your sample data to draw conclusions about the entire population. When you collect a random sample, you might observe an effect within the sample, such as a difference between group means. But, does that effect exist in the population? Or is it just random error in the sample?

For example, suppose you’re comparing two teaching methods and determining whether one produces higher mean test scores. In your sample data, you see that the mean for Method A is greater than Method B. However, random samples contain random error, which makes your sample means very unlikely to equal the population means precisely. Unfortunately, the difference between the sample means of two teaching methods can represent either an effect in the population or random error in your sample.

This point is where p-values and significance levels come in. Typically, you want p-values that are less than your significance levels (e.g., $0.05$ ) because it indicates your sample evidence is strong enough to conclude that Method A is better than Method B for the entire population. The teaching method appears to have a real effect. Exciting stuff!

However, I’ll go in the opposite direction for this section and try to help you appreciate higher, insignificant p-values! These are cases where you cannot conclude that an effect exists in the population. For the teaching method example above, a higher p-value indicates that we have insufficient evidence to find that one teaching method is better than the other.

Let’s graphically illustrate three different hypothetical studies about teaching methods in the plots below. Which of the following three studies have statistically significant results? The difference between the two groups is the effect size for each experiment. Here’s the CSV data file: $\text { studies }$.

All three studies appear to have differences between their sample means. However, even if the population means are equal, the sample means are unlikely to be equal. We need to filter out the signal (real differences) from the noise (random error). That’s where hypothesis tests play a role.

统计代写|假设检验作业代写HYPOTHESIS TESTING代考|Practical vs. Statistical Significance

In the previous section, we looked at how a relatively large effect in your sample might really be random error. We saw how high p-values can protect you from jumping to conclusions based on the error. In this section, I help you avoid the opposite condition.

Imagine you’ve just performed a hypothesis test and your results are statistically significant. Hurray! These results are important, right? Not so fast. Statistical significance does not necessarily mean that the results are practically meaningful in the real world. You can have significant results for a small effect. Remember how the previous section showed how effect size was only one factor out of three?

In this section, I’ll talk about the differences between practical significance and statistical significance, and how to determine if your results are meaningful in the real world.
Statistical Significance
The hypothesis testing procedure determines whether the sample results that you obtain are likely if you assume the null hypothesis is correct for the population. If the results are sufficiently improbable under that assumption, you can reject the null hypothesis and conclude that an effect exists. In other words, the strength of the evidence in your sample has passed your defined threshold of the significance level (alpha). Your results are statistically significant.

Consequently, it might seem logical that p-values and statistical significance relate to importance. However, that is false because conditions other than large effect sizes can produce tiny p-values.

Hypothesis tests with small effect sizes can produce very low p-values when you have a large sample size and/or the data have low variability. Consequently, effect sizes that are trivial in the practical sense can be statistically significant.
Here’s how small effect sizes can still produce tiny p-values:
You have a very large sample size. As the sample size increases, the hypothesis test gains greater statistical power to detect small effects. With a large enough sample size, the hypothesis test can detect an effect that is so miniscule that it is meaningless in a practical sense.
The sample variability is very low. When your sample data have low variability, hypothesis tests can produce more precise estimates of the population’s effect. This precision allows the test to detect tiny effects.

Statistical significance indicates only that you have sufficient evidence to conclude that an effect exists. It is a mathematical definition that does not know anything about the subject area and what constitutes an important effect.
Practical Significance
Size matters!
While statistical significance relates to whether an effect exists, practical significance refers to its magnitude. However, no statistical test can tell you whether the effect is large enough to be important in your field of study. Instead, you need to apply your subject area knowledge and expertise to determine whether the effect is big enough to be meaningful in the real world. In other words, is it large enough to care about?

How do you do this? I find that it is helpful to identify the smallest effect size that still has some practical significance. Again, this process determine whether all, some, or none of that range represents practically significant effects.

统计代写|假设检验作业代写HYPOTHESIS TESTING代考|Practical Tips to Avoid Being Fooled

After reading the chapter to this point, you should have no doubts that understanding your hypothesis test result is not as simple as only whether your p-value is less than your significance level. Now, I’ll build on the information we covered throughout this chapter and present practical advice that helps you assess and minimize the possibility of being fooled by false positives and other misleading results.

Previously, I showed how a common misconception about interpreting p-values produces the illusion of substantially more evidence against the null hypothesis than is justified. For example, a p-value near $0.05$ often has a false positive error rate of between $23-50 \%$. These greater than expected false positive rates create doubts about trusting statistically significant results. Relatedly, we saw how the reproducibility rate for psychology studies is surprisingly low.

When a hypothesis test produces significant results, there is always that chance that it is a false positive. In this context, a false positive occurs when you obtain a statistically significant p-value, and you unknowingly reject a null hypothesis that is actually true. You conclude that an effect exists in the population when it does not exist.

From a scientific point of view, the high false-positive rates are problematic because of the misleading results. From a practical standpoint, if you are using a hypothesis test to improve a product or process, you won’t obtain the benefits that you expect if the test results are a false positive. That can cost you a lot of money!

Let’s delve into the tips. These tips will help you develop a deeper understanding of your test results. I’ll use a real AIDS vaccine study conducted in Thailand to work through these considerations. The study obtained a p-value of $0.039$, which sounds great. Hurray, the vaccine works! However, after reading this book, you might think differently.

统计代写| 假设检验作业代写Hypothesis testing代考|The Good Side of High P-values

假设检验代写

统计代写| 假设检验作业代写HYPOTHESIS TESTING代考|THE GOOD SIDE OF HIGH P-VALUES

高 p 值会有帮助吗?高 p 值是什么意思?
通常,当您执行假设检验时,您希望获得具有统计显着性的低 p 值。小的 p 值很性感。它们代表了令人兴奋的发现,可以帮助您发表文章。
但是,您可能会惊讶地发现,在统计上不显着的较高 p 值也很有价值。在本节中,我将向您展示 p 值大于0.05,或您使用的任何显着性级别。

假设检验是推理统计的一种形式。您想使用您的样本数据得出关于整个人口的结论。当您收集随机样本时,您可能会观察到样本内的效应,例如组均值之间的差异。但是,这种影响是否存在于人群中?还是只是样本中的随机错误?

例如,假设您正在比较两种教学方法并确定一种方法是否会产生更高的平均考试成绩。在您的样本数据中,您会看到方法 A 的均值大于方法 B。但是,随机样本包含随机误差,这使得您的样本均值不太可能精确地等于总体均值。不幸的是,两种教学方法的样本均值之间的差异可能代表总体效应或样本中的随机误差。

这就是 p 值和显着性水平的用武之地。通常,您希望 p 值小于显着性水平和.G.,$0.05$因为它表明您的样本证据足够强大,可以得出结论,对于整个人群来说,方法 A 优于方法 B。这种教学方法似乎有真正的效果。激动人心的东西!

但是,我将在本节中采取相反的方向,并尝试帮助您了解更高、微不足道的 p 值!在这些情况下,您无法得出总体中存在效应的结论。对于上面的教学方法示例,较高的 p 值表明我们没有足够的证据表明一种教学方法优于另一种教学方法。

让我们在下图中以图形方式说明关于教学方法的三种不同假设研究。以下三项研究中哪一项具有统计学意义?两组之间的差异是每个实验的效果大小。这是 CSV 数据文件:$ \text { studies } $.

所有三项研究的样本均值似乎都存在差异。但是,即使总体均值相等,样本均值也不太可能相等。我们需要过滤掉信号r和一种一世d一世FF和r和nC和s从噪音r一种nd○米和rr○r. 这就是假设检验发挥作用的地方。

统计代写|假设检验作业代写HYPOTHESIS TESTING代考|PRACTICAL VS. STATISTICAL SIGNIFICANCE

在上一节中,我们研究了样本中相对较大的影响实际上可能是随机误差。我们看到了高 p 值如何保护您免于根据错误得出结论。在本节中,我将帮助您避免相反的情况。

想象一下,您刚刚进行了假设检验,并且您的结果具有统计显着性。欢呼!这些结果很重要,对吧?没那么快。统计意义并不一定意味着结果在现实世界中具有实际意义。您可以获得显着的效果,效果很小。还记得上一节如何显示效果大小只是三分之一的因素吗?

在本节中,我将讨论实际显着性和统计显着性之间的差异,以及如何确定您的结果在现实世界中是否有意义。
统计显着
性 假设检验过程确定如果您假设原假设对总体正确,则您获得的样本结果是否可能。如果在该假设下结果不太可能,您可以拒绝原假设并得出存在效应的结论。换句话说,您的样本中证据的强度已经超过了您定义的显着性水平阈值一种一世pH一种. 您的结果具有统计学意义。

因此,p 值和统计显着性与重要性相关似乎是合乎逻辑的。然而,这是错误的,因为除了大效应大小之外的条件会产生微小的 p 值。

当样本量很大和/或数据的可变性较低时,效应量较小的假设检验会产生非常低的 p 值。因此,在实际意义上微不足道的效应量可能具有统计学意义。
以下是较小的效应量仍然可以产生微小的 p 值:
您有一个非常大的样本量。随着样本量的增加,假设检验获得更大的统计功效来检测小的影响。如果样本量足够大,假设检验可以检测到一种非常微小的影响,以至于它在实际意义上毫无意义。
样本变异性非常低。当您的样本数据的变异性较低时,假设检验可以对总体效应产生更精确的估计。这种精度使测试能够检测到微小的影响。

统计显着性仅表明您有足够的证据可以得出存在影响的结论。这是一个数学定义,对主题领域和重要影响的构成一无所知。
实际意义
尺寸很重要!
虽然统计显着性与效应是否存在有关,但实际显着性是指其大小。但是,没有统计测试可以告诉您影响是否大到足以在您的研究领域中发挥重要作用。相反,您需要应用您的学科领域知识和专业知识来确定影响是否大到足以在现实世界中有意义。换句话说,它足够大吗?

你怎么做到这一点?我发现确定仍然具有一定实际意义的最小效应量是有帮助的。同样,此过程确定该范围的全部、部分或不代表实际显着的影响。

统计代写|假设检验作业代写HYPOTHESIS TESTING代考|PRACTICAL TIPS TO AVOID BEING FOOLED

读完本章至此,你应该不会怀疑,理解你的假设检验结果并不只是你的 p 值是否小于你的显着性水平那么简单。现在,我将在本章介绍的信息的基础上提供实用建议,帮助您评估并最大限度地减少被误报和其他误导性结果所欺骗的可能性。

之前,我展示了关于解释 p 值的常见误解如何产生一种错觉,即反对零假设的证据比合理的要多得多。例如,p 值接近0.05误报率通常介于23−50%. 这些高于预期的误报率让人怀疑是否信任具有统计意义的结果。与此相关的是,我们看到心理学研究的重现率低得惊人。

当假设检验产生显着结果时,总是有可能是假阳性。在这种情况下,当您获得具有统计意义的 p 值并且您在不知不觉中拒绝了实际上为真的原假设时,就会出现误报。您得出结论,当效应不存在时,总体中存在效应。

从科学的角度来看,高假阳性率是有问题的,因为结果具有误导性。从实际的角度来看,如果您使用假设检验来改进产品或流程,如果测试结果为假阳性,您将不会获得预期的好处。这可能会花费你很多钱!

让我们深入研究这些技巧。这些提示将帮助您更深入地了解您的测试结果。我将使用在泰国进行的一项真正的艾滋病疫苗研究来解决这些问题。该研究获得的 p 值为0.039,听起来很棒。万岁,疫苗有效!但是,读完这本书后,你可能会有不同的想法。

统计代写| 假设检验作业代写Hypothesis testing代考|Population Parameters vs. Sample Statistics 请认准UprivateTA™. UprivateTA™为您的留学生涯保驾护航。

统计代考

统计是汉语中的“统计”原有合计或汇总计算的意思。 英语中的“统计”(Statistics)一词来源于拉丁语status,是指各种现象的状态或状况。

数论代考

数论(number theory ),是纯粹数学的分支之一,主要研究整数的性质。 整数可以是方程式的解(丢番图方程)。 有些解析函数(像黎曼ζ函数)中包括了一些整数、质数的性质,透过这些函数也可以了解一些数论的问题。 透过数论也可以建立实数和有理数之间的关系,并且用有理数来逼近实数(丢番图逼近)

数值分析代考

数值分析(Numerical Analysis),又名“计算方法”,是研究分析用计算机求解数学计算问题的数值计算方法及其理论的学科。 它以数字计算机求解数学问题的理论和方法为研究对象,为计算数学的主体部分。

随机过程代写

随机过程,是依赖于参数的一组随机变量的全体,参数通常是时间。 随机变量是随机现象的数量表现,其取值随着偶然因素的影响而改变。 例如,某商店在从时间t0到时间tK这段时间内接待顾客的人数,就是依赖于时间t的一组随机变量,即随机过程

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。

Related Posts

Leave a comment