statistical postulates; statistical hypothesis

简明释义

统计假设

英英释义

例句

1.In a research study, we must first establish the statistical postulates 统计公设 before testing our statistical hypothesis 统计假设.

在研究中，我们必须首先建立统计公设，然后再测试我们的统计假设。

2.The validity of our conclusions relies heavily on the statistical postulates 统计公设 we assume in our analysis.

我们结论的有效性在很大程度上依赖于我们分析中假设的统计公设。

3.To prove our statistical hypothesis 统计假设, we need to collect sufficient data that aligns with the statistical postulates 统计公设.

为了证明我们的统计假设，我们需要收集与统计公设一致的足够数据。

4.Researchers often challenge existing statistical postulates 统计公设 when new evidence contradicts them.

当新证据与现有的统计公设相矛盾时，研究人员常常会质疑这些公设。

5.Before formulating your statistical hypothesis 统计假设, ensure that the underlying statistical postulates 统计公设 are sound.

在制定你的统计假设之前，确保基础的统计公设是合理的。

作文

In the realm of statistics, two fundamental concepts that often arise are statistical postulates and statistical hypothesis. Understanding these terms is crucial for anyone delving into the world of data analysis or research. Statistical postulates, or basic assumptions, form the foundation upon which statistical theories and methodologies are built. They are the underlying principles that guide researchers in their analysis and interpretation of data. For instance, one common statistical postulate is the assumption that a sample is representative of the population from which it is drawn. This means that the characteristics of the sample should reflect those of the larger population, ensuring that conclusions drawn from the sample can be generalized to the whole population.On the other hand, a statistical hypothesis is a statement or an assumption about a parameter in a population. It is a claim that can be tested using statistical methods. Hypotheses are generally formulated in pairs: the null hypothesis (denoted as H0) represents a statement of no effect or no difference, while the alternative hypothesis (denoted as H1 or Ha) represents a statement of an effect or a difference. For example, if researchers want to test whether a new drug is effective, they might set up the null hypothesis as "the drug has no effect on patients" and the alternative hypothesis as "the drug has a positive effect on patients".The relationship between statistical postulates and statistical hypothesis is essential in the field of inferential statistics. When researchers formulate a hypothesis, they often rely on certain statistical postulates to ensure that their testing methods are valid. For example, one common postulate is that the data should follow a normal distribution when conducting parametric tests. If this assumption is violated, the results of hypothesis testing may not be reliable.Moreover, understanding these concepts helps in the interpretation of results. Once a hypothesis is tested, researchers must determine whether to reject or fail to reject the null hypothesis based on the evidence provided by the data. This decision-making process is influenced by the statistical postulates assumed during the analysis. If the postulates hold true, the conclusions drawn from the hypothesis test are more likely to be valid and applicable to the population at large.In conclusion, mastering the concepts of statistical postulates and statistical hypothesis is vital for anyone involved in research and data analysis. These concepts not only provide a framework for conducting statistical tests but also guide the interpretation of results. By understanding the assumptions that underpin statistical methodologies, researchers can ensure that their findings are robust and meaningful. As we continue to navigate an increasingly data-driven world, the importance of these statistical foundations cannot be overstated.

在统计学领域，两个经常出现的基本概念是统计公设和统计假设。理解这些术语对于任何深入数据分析或研究的人来说都是至关重要的。统计公设，或基本假设，构成了统计理论和方法论的基础。它们是指导研究人员分析和解释数据的基本原则。例如，一个常见的统计公设是样本代表所抽取的总体的假设。这意味着样本的特征应该反映更大总体的特征，从而确保从样本得出的结论可以推广到整个总体。另一方面，统计假设是关于总体参数的陈述或假设。它是一个可以通过统计方法进行检验的声明。假设通常成对提出：零假设（记作H0）代表没有效应或没有差异的陈述，而备择假设（记作H1或Ha）代表有影响或有差异的陈述。例如，如果研究人员想要测试一种新药是否有效，他们可能会将零假设设定为“该药对患者没有影响”，而将备择假设设定为“该药对患者有积极影响”。统计公设和统计假设之间的关系在推断统计学领域至关重要。当研究人员制定假设时，他们通常依赖于某些统计公设来确保其检验方法的有效性。例如，一个常见的公设是，在进行参数检验时，数据应遵循正态分布。如果这一假设被违反，假设检验的结果可能不可靠。此外，理解这些概念有助于结果的解释。一旦假设被检验，研究人员必须根据数据提供的证据来决定是拒绝还是未能拒绝零假设。这一决策过程受到分析中假设的统计公设的影响。如果公设成立，从假设检验得出的结论更可能是有效的，并且适用于更大的人群。总之，掌握统计公设和统计假设的概念对于参与研究和数据分析的任何人都是至关重要的。这些概念不仅为进行统计检验提供了框架，还指导了结果的解释。通过理解支撑统计方法论的假设，研究人员可以确保他们的发现是稳健和有意义的。随着我们继续在日益数据驱动的世界中航行，这些统计基础的重要性不可低估。