A statistical Hypothesis is a belief made about a population parameter. This belief may or might not be right. In other words, hypothesis testing is a proper technique utilized by scientist to support or reject statistical hypotheses. The foremost ideal approach to decide if a statistical hypothesis is correct is to examine the whole population.

统计 假设是关于总体参数的一种信念。 这种信念可能是正确的，也可能不是正确的。 换句话说，假设检验是科学家用来支持或拒绝统计假设的一种适当技术。 决定统计假设是否正确的最理想的方法是检查整个人口。

Since that’s frequently impractical, we normally take a random sample from the population and inspect the equivalent. Within the event sample data set isn’t steady with the statistical hypothesis, the hypothesis is refused.

由于这通常是不切实际的，因此我们通常从总体中随机抽取一个样本并检查等效样本。 如果事件样本数据集的统计假设不稳定，则拒绝该假设。

假设类型 (Types of hypothesis)

There are two sorts of hypothesis and both the Null Hypothesis (Ho) and Alternative Hypothesis (Ha) must be totally mutually exclusive events.

假说有两种， 空假说 (Ho)和替代假说 (Ha)必须是完全互斥的事件。

• Null hypothesis is usually the hypothesis that the event won't happen.

•空假设通常是事件不会发生的假设。

• Alternative hypothesis is a hypothesis that the event will happen.

•替代假设是事件将发生的假设。

为什么我们需要假设检验？ (Why we need Hypothesis Testing?)

Suppose a company needs to launch a new bicycle in the market. For this situation, they will follow Hypothesis Testing all together decide the success of the new product in the market.

假设一家公司需要在市场上推出一款新自行车。 对于这种情况，他们将一起进行假设检验，共同决定新产品在市场上的成功。

Where the likelihood of the product being ineffective in the market is undertaken as the Null Hypothesis and the likelihood of the product being profitable is undertaken as an Alternative Hypothesis. By following the process of Hypothesis testing they will foresee the accomplishment.

将产品在市场上无效的可能性作为零假设，而将产品获利的可能性作为替代假设。 通过遵循假设检验的过程，他们将预见其成就。

如何计算假设检验？ (How to Calculate Hypothesis Testing?)

· State the two theories with the goal that just one can be correct, to such an extent that the two occasions are totally unrelated.

·陈述两种理论，目标是只有一种是正确的，以至于两种情况完全无关。

· Now figure a study plan, that will lay out how the data will be assessed.

·现在制定一个研究计划，该计划将列出如何评估数据。

· Now complete the plan and genuinely investigate the sample dataset.

·现在，完成计划并真正调查样本数据集。

· Finally examine the outcome and either accept or reject the null hypothesis.

·最后检查结果，并接受或拒绝原假设。

另一个例子 (Another example)

Assume, a person has gone after a job and he has expressed in the resume that his composing speed is 70 words per minute. The recruiter might need to test his case. On the off chance that he sees his case as adequate, he will enlist him, in any case, reject him. Thus, after the test and found that his speed is 63 words a minute. Presently, he can settle on whether to employ him or not. In the event that he meets all other qualification measures. This procedure delineates Hypothesis Testing in layman’s terms.

假设一个人去找工作了，他在简历中表示自己的写作速度是每分钟70个单词。 招聘人员可能需要测试他的情况。 在他认为自己的案子足够的偶然机会上，他将征召他，无论如何，拒绝他。 这样，经过测试，发现他的速度是每分钟63个字。 目前，他可以决定是否雇用他。 如果他符合所有其他资格评定标准。 此过程以外行的术语描述了假设检验。

In statistical terms Hypothesis, his composing speed is 70 words per minute is a hypothesis to be tested so-called null hypothesis. Clearly, the alternating hypothesis his composing speed isn’t 70 words per minute. So, normal composing speed is the population parameter and sample composing speed is sample statistics.

用统计学的假设来说，他的写作速度是每分钟70个单词，这是一个需要检验的假设，即所谓的零假设。 显然，他的写作速度不是每分钟70个单词。 因此，正常合成速度是总体参数，样本合成速度是样本统计量。

The conditions of accepting or rejecting his case are to be chosen by the selection representative. For instance, he may conclude that an error of 6 words is alright to him so he would acknowledge his claim between 64 to 76 words per minute. All things considered, sample speed 63 words per minute will close to reject his case. Furthermore, the choice will be he was producing a fake claim.

selection选代表应选择接受或拒绝其案件的条件。 例如，他可能会得出结论，认为6个字的错误对他来说是可以的，因此他将承认他的要求是每分钟64到76个字。 考虑到所有因素，采样速度为每分钟63个单词将接近拒绝他的案件。 此外，选择将是他提出了虚假主张。

In any case, if the selection representative stretches out his acceptance region to positive/negative 7 words that are 63 to 77 words, he would be tolerating his case. In this way, to finish up, Hypothesis Testing is a procedure to test claims about the population dependent on the sample. It is a fascinating reasonable subject with a quite statistical jargon. You have to dive more to get familiar with the details.

无论如何，如果the选代表将他的接受范围扩展到63到77个单词的正/负7个单词，那么他将容忍自己的情况。 通过这种方式，最后，假设检验是一种测试关于依赖样本的总体的声明的过程。 这是一个引人入胜的合理主题，而且具有相当的统计术语。 您必须花更多精力去熟悉细节。

假设的显着性水平和排斥区域 (Significance Level and Rejection Region for Hypothesis)

Type I error probability is normally indicated by α and generally set to 0.05. The value of α is recognized as the significance level.

I型错误概率通常由α表示，通常设置为0.05。 α的值被认为是显着性水平 。

The rejection region is the set of sample data that prompts the rejection of the null hypothesis. The significance level, α, decides the size of the rejection region. Sample results in the rejection region are labelled statistically significant at the level of α.

拒绝区域是一组样本数据，提示拒绝原假设。 显着性水平α决定了拒绝区域的大小。 剔除区域的样品结果在α水平上被标记为具有统计学意义。

The impact of differing α is that If α is small, for example, 0.01, the likelihood of a type I error is little, and a ton of sample evidence for the alternative hypothesis is needed before the null hypothesis can be dismissed. Though, when α is bigger, for example, 0.10, the rejection region is bigger, and it is simpler to dismiss the null hypothesis.

不同的α的影响在于，如果α小(例如0.01)，则I型错误的可能性很小，并且在可以驳回原假设之前，需要大量的替代假设样本证据。 但是，当α较大(例如0.10)时，拒绝区域较大，并且更容易消除原假设。

p值的意义 (Significance from p-values)

A subsequent methodology is to evade the utilization of a significance level and rather just report how significant the sample evidence is. This methodology is as of now more widespread. It is accomplished by the method of a P-value. P-value is a gauge of power of the evidence against the null hypothesis. It is the likelihood of getting the observed value of test statistic, or value with significantly more prominent proof against the null hypothesis (Ho) if the null hypothesis of an investigation question is true. The less significant the P-value, the more proof there is supportive of the alternative hypothesis. Sample evidence is measurably noteworthy at the α level just if the P-value is less than α. They have an association for two-tail tests. When utilizing a confidence interval to playout a two-tailed hypothesis test, reject the null hypothesis if and just if the hypothesized value doesn’t lie inside a confidence interval for the parameter.

随后的方法是逃避对显着性水平的利用，而只是报告样本证据的显着性。 截止到现在，这种方法更加广泛。 它是通过P值的方法完成的。 P值是针对原假设的证据效力的量度。 如果调查问题的原假设是真实的，则有可能获得检验统计量的观察值，或获得对原假设(Ho)具有明显更显着证明的值。 P值的意义越小，证明替代假设的证据越多。 即使P值小于α，样本证据在α水平上也相当可观。 他们有两个尾巴测试的关联。 当使用置信区间播放二尾假设检验时，如果且仅当假设值不在参数的置信区间内时，拒绝原假设。

假设检验和置信区间 (Hypothesis Tests and Confidence Intervals)

Hypothesis tests and confidence intervals are cut out of the same cloth. An event whose 95% confidence interval reject the hypothesis is an event for which p<0.05 under the relating hypothesis test, and the other way around. A P-value is letting you know the greatest confidence interval that despite everything prohibits the hypothesis. As such, if p<0.03 against the null hypothesis, that implies that a 97% confidence interval does exclude the null hypothesis.

假设检验和置信区间是从同一块布上剪下来的。 95％置信区间拒绝该假设的事件是在相关假设检验下p <0.05的事件，反之亦然。 P值让您知道最大的置信区间，尽管所有情况都阻止了该假设。 这样，如果针对原假设的p <0.03，则意味着97％的置信区间确实排除了原假设。

总体均值的假设检验 (Hypothesis Tests for a Population Mean)

We do a T-test on the ground that the population mean is unknown. The general purpose is to contrast sample mean with some hypothetical population mean, to assess whether the watched the truth is such a great amount of unique in relation to the hypothesis that we can say with assurance that the hypothetical population mean isn’t, indeed, the real population mean.

我们以总体均值未知为由进行T检验。 一般目的是将样本均值与某些假设总体均值进行对比，以评估观察到的真相与假设是否有如此多的独特性，我们可以肯定地说，假设总体均值并不是，实际人口平均数。

人口比例假设检验 (Hypothesis Tests for a Population Proportion)

At the point when you have two unique populations Z test facilitates you to choose if the proportion of certain features is the equivalent or not in the two populations. For instance, if the male proportion is equivalent between the two nations.

当您有两个唯一的总体时， Z检验可帮助您选择某些特征的比例在两个总体中是否相等。 例如，如果两国之间的男性比例相等。

均等人口方差假设检验 (Hypothesis Test for Equal Population Variances)

F Test depends on F distribution and is utilized to think about the variance of the two impartial samples. This is additionally utilized with regards to the investigation of variance for making a decision about the significance of more than two samples.

F检验取决于F分布，并用于考虑两个公正样本的方差。 关于方差研究，还可以利用它来决定两个以上样本的重要性。

T检验，F检验和Z检验 (T-test, F-test and Z-test)

T-test and F test are totally two unique things. The T-test is utilized to evaluate the population parameter, for example, the population mean, and is likewise utilized for hypothesis testing for a population mean. However, it must be utilized when we don’t know about the population standard deviation. On the off chance that we know the population standard deviation, we will utilize the Z test. We can likewise utilize T statistic to approximate population mean. T statistic is likewise utilised for discovering the distinction in two population means with the assistance of sample means.

T检验和F检验完全是两件事。 T检验用于评估总体参数，例如总体平均值，并且同样用于总体平均值的假设检验。 但是，当我们不了解总体标准偏差时，必须使用它。 如果我们知道总体标准偏差，我们将使用Z检验。 我们同样可以利用T统计量来近似总体均值。 同样，在样本均值的帮助下，利用T统计量来发现两个总体均值之间的区别。

Z statistic or T statistic is utilized to assess population parameters such as population mean and population proportion. It is likewise used for testing hypothesis for population mean and population proportion. In contrast to Z statistic or T statistic, where we manage mean and proportion, Chi-Square or F test is utilized for seeing if there is any variance inside the samples. F test is the proportion of fluctuation of two samples.

Z统计量或T统计量用于评估总体参数，例如总体平均值和总体比例。 它同样用于检验人口均值和人口比例的假设。 与我们管理均值和比例的Z统计量或T统计量相比，卡方检验或F检验用于查看样本内部是否存在任何方差。 F检验是两个样本的波动比例。

结论 (Conclusion)

Hypothesis encourages us to make coherent determinations, the connection among variables and gives the course to additionally investigate. Hypothesis, for the most part, results from speculation concerning studied behaviour, natural phenomenon, or proven theory. An honest hypothesis ought to be clear, detailed, and reliable with the data. In the wake of building up the hypothesis, the following stage is validating or testing the hypothesis. Testing of hypothesis includes the process that empowers to concur or differ with the expressed hypothesis.

假设鼓励我们做出连贯的决定，确定变量之间的联系，并提供进一步研究的过程。 大多数情况下，假设是由对所研究的行为，自然现象或经验证的理论的推测得出的。 诚实的假设应该对数据清楚，详细和可靠。 在建立假设之后，接下来的阶段是验证或检验假设。 假设检验包括授权与所表达的假设一致或不同的过程。

Written by:

撰写人：

Saurav Singla

绍拉夫·辛格拉