In probability theory and statistics, the -distribution with degrees of freedom is the distribution of a sum of the squares of independent standard normal random variables. [2] The chi-squared distribution is a special case of the gamma distribution and the univariate Wishart distribution.

A chi-squared test (also chi-square or χ 2 test) is a statistical hypothesis test used in the analysis of contingency tables when the sample sizes are large. In simpler terms, this test is primarily used to examine whether two categorical variables ( two dimensions of the contingency table ) are independent in influencing the test statistic ...

卡方分布（英語： chi-square distribution [2], χ ²-distribution ，或寫作 χ ²分布）是概率论与统计学中常用的一种概率分布。k个独立的标准正态分布变量的平方和服从自由度为k的卡方分布。

2023年8月29日 · The Chi-square test is a statistical method used to determine if there's a significant association between two categorical variables in a sample.

2022年5月23日 · Calculate the chi-square value from your observed and expected frequencies using the chi-square formula. Find the critical chi-square value in a chi-square critical value table or using statistical software.

2022年2月11日 · 卡方检验（Chi-Square Test）是一类基于卡方分布的假设检验方法，常用于检验两个离散变量是否存在关联（独立性检验）、理论分布与观测分布是否一致（适配度检验）、以及在金融风控、医学研究、社会科学调查等领域广泛应用。

2022年5月20日 · Chi-square (Χ 2) distributions are a family of continuous probability distributions. They’re widely used in hypothesis tests, including the chi-square goodness of fit test and the chi-square test of independence. The shape of a chi-square distribution is determined by the parameter k, which represents the degrees of freedom.

独立性检验是统计学的一种检验方式，与适合性检验同属于X2检验，即卡方检验（英文名：chi square test），它是根据次数资料判断两类因子彼此相关或相互独立的假设检验。

A chi-square test for independence compares two variables in a contingency table to see if they are related. In a more general sense, it tests to see whether distributions of categorical variables differ from each another.

2025年3月6日 · The Chi-Square test is a statistical procedure for determining the difference between observed and expected data. It can also be used to decide whether the data correlates with our categorical variables. Thus, it helps determine whether a difference between two categorical variables is due to chance or a relationship between them.