As an expert in statistical analysis, I would like to clarify the concept of hypothesis testing, which is a fundamental process in inferential statistics. Hypothesis testing involves making inferences about a population based on a sample of data. It's a systematic approach to deciding whether the results of a study are due to chance or if there's a statistically significant effect. The process involves setting up a null hypothesis (H0) and an alternative hypothesis (H1), collecting data, and then using statistical methods to evaluate the evidence.

The null hypothesis is a statement of no effect or no difference. It's a default position that assumes there is no significant relationship between the variables being studied or that a treatment has no effect. The alternative hypothesis, on the other hand, is what you might believe to be true or what you're testing for. It asserts that there is a significant effect or a difference.

The p-value is a critical component in hypothesis testing. It represents the probability, assuming the null hypothesis is true, of observing a result at least as extreme as the test statistic calculated from your sample data. If the p-value is very low, it suggests that the results are unlikely to have occurred by chance alone, and you may reject the null hypothesis in favor of the alternative.

A statistical significance test is a method used to decide whether the null hypothesis should be rejected or not. It involves comparing the p-value to a predetermined level of significance, often denoted as alpha (α). If the p-value is less than α, the result is considered statistically significant, and the null hypothesis is rejected.

The concept of hypothesis testing has its origins in the early 20th century with the work of statistician Ronald Fisher. Fisher introduced the idea of p-values and significance testing as a way to make decisions about the validity of scientific claims based on data.

Now, let's translate the above explanation into Chinese.

统计分析领域的专家会向您阐明假设检验的概念，这是推断统计学中的一个基本过程。假设检验涉及基于数据样本对总体做出推断。它是一种系统化的方法，用于决定研究结果是由于偶然性还是存在统计学上显著的效果。这个过程包括设定一个零假设（H0）和一个备择假设（H1），收集数据，然后使用统计方法来评估证据。

零假设是一种无效应或无差异的陈述。它是一个默认立场，假设正在研究的变量之间没有显著的关系，或者治疗没有效果。另一方面，备择假设是您可能认为真实的或者是您正在测试的内容。它断言存在显著的效果或差异。

p值是假设检验中的关键组成部分。它代表了假设零假设为真的情况下，观察到至少与从样本数据计算出的检验统计量一样极端的结果的概率。如果p值非常低，这表明结果不太可能仅由偶然性发生，您可能会拒绝零假设，支持备择假设。

统计显著性检验是用于决定是否应该拒绝零假设的方法。它涉及将p值与预先确定的显著性水平进行比较，通常表示为α。如果p值小于α，结果被认为是统计学上显著的，零假设被拒绝。

假设检验的概念起源于20世纪初统计学家罗纳德·费希尔的工作。费希尔引入了p值和显著性检验的概念，作为基于数据对科学主张的有效性做出决策的一种方式。

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p-value. The probability, assuming the null hypothesis is true, of observing a result at least as extreme as the test statistic. Statistical significance test. A predecessor to the statistical hypothesis test (see the Origins section).read more >>