normality test

简明释义

正规性检验

英英释义

例句

1.In order to validate our assumptions, we need to run a normality test 正态性检验 on the sample data.

为了验证我们的假设，我们需要对样本数据进行normality test 正态性检验。

2.A common method for performing a normality test 正态性检验 is the Shapiro-Wilk test.

执行normality test 正态性检验的常用方法是Shapiro-Wilk检验。

3.Before conducting a t-test, it's essential to perform a normality test 正态性检验 to ensure the data follows a normal distribution.

在进行t检验之前，进行normality test 正态性检验是必要的，以确保数据遵循正态分布。

4.The results of the normality test 正态性检验 indicated that the dataset was not normally distributed.

normality test 正态性检验的结果表明数据集并不符合正态分布。

5.The normality test 正态性检验 results showed a p-value less than 0.05, indicating non-normality.

该normality test 正态性检验的结果显示p值小于0.05，表明数据不符合正态性。

作文

In the field of statistics, understanding data distribution is crucial for making informed decisions. One of the essential tools used by statisticians to assess whether a dataset follows a normal distribution is the normality test. A normality test (正态性检验) helps determine if the data can be approximated by a normal distribution, which is a foundational concept in statistics. This is important because many statistical methods, including t-tests and ANOVA, assume that the data are normally distributed. Therefore, conducting a normality test is a vital step before applying these techniques.There are several methods to perform a normality test. Commonly used tests include the Shapiro-Wilk test, the Kolmogorov-Smirnov test, and the Anderson-Darling test. Each of these tests has its own strengths and weaknesses, but they all serve the same purpose: to evaluate the hypothesis that a dataset is normally distributed. For instance, the Shapiro-Wilk test is particularly powerful for small sample sizes, while the Kolmogorov-Smirnov test can be used for larger datasets.When performing a normality test, the null hypothesis typically states that the data follows a normal distribution. If the test returns a p-value below a predetermined significance level, often set at 0.05, we reject the null hypothesis, concluding that the data does not follow a normal distribution. Conversely, if the p-value is above this threshold, we fail to reject the null hypothesis, suggesting that the data may be normally distributed.Understanding the results of a normality test is essential for proper data analysis. If the data is found to be non-normally distributed, statisticians may need to consider alternative methods or transformations. For example, applying a logarithmic transformation can sometimes help stabilize variance and make the data more closely resemble a normal distribution. Alternatively, non-parametric statistical methods can be employed, which do not require the assumption of normality.Moreover, visual assessments such as Q-Q plots and histograms can complement formal normality tests. These graphical methods provide an intuitive understanding of how data compares to a normal distribution. A Q-Q plot, for example, displays the quantiles of the dataset against the quantiles of a standard normal distribution. If the points on the plot fall approximately along a straight line, it suggests that the data is normally distributed.In conclusion, the normality test (正态性检验) is a fundamental aspect of statistical analysis, guiding researchers in determining the appropriate methods for their data. By assessing whether data adheres to a normal distribution, statisticians can ensure that their analyses yield valid and reliable results. As data-driven decision-making continues to grow in importance across various fields, mastering the use of normality tests will be invaluable for anyone involved in statistical analysis.