独立样本的t统计量
1.独立样本t检验的假设
- 虚无假设（H0：μ1-μ2=0）：独立样本所来自的两个总体的均值之间没有显著的差异，即所抽取的两个样本来自同一个总体。
- 备择假设（H1：μ1-μ2≠0）：独立样本所来自的两个总体的均值之间有显著差异。
2.总体方差的估计
总体方差的合并估计值为
- 估计的思路：将两样本进行加权平均，权重是样本的自由度
- 估计的前提：两样本方差大体相等（即满足方差同质性）
3.均值分布的方差的计算
- 计算的思路：由于样本容量n存在差异，两个样本均值的分布不一定相同，因此均值分布的变异性需要考虑样本容量。
4.标准误的计算
- 计算的思路：均值差异样本的方差是总体1与总体2的均值分布的方差之和。
5.t统计量的计算
与单样本T检验有三点不同之处
- 比较的分布是均值差异的分布（X1-X2）
- 确定t的临界值是基于两个样本的自由度（df1+df2）
- 比较分布的样本分数是基于两个分数之差
1.Hypothesis of independent sample t-test
- The null hypothesis (H0: μ1- μ2=0): There is no significant difference between the means of the two populations from which the independent samples come, indicating that the two samples are from the same population.
- Alternative hypothesis (H1: μ1- μ2≠0): There is a significant difference between the means of two populations from which independent samples come.
2.Estimation of population variance
The combined estimated value of the overall variance is
- Estimation approach: Perform a weighted average of two samples, with the weight being the degrees of freedom of the samples.
- Assumption for estimation: The variance of the two samples is roughly equal (i.e. satisfies the homogeneity of variance)
3.Calculation of variance of mean distribution
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- Calculation approach: Due to differences in sample size n, the distribution of the mean values between the two samples may not be the same. Therefore, the variability of the mean distribution needs to consider the sample size.
4.Calculation of standard error
- Calculation idea: The variance of the mean difference sample is the sum of the variances of the mean distributions of population 1 and population 2.
5.Calculation of t-statistic
There are three differences from single sample T-test
- The distribution of comparison is the distribution of mean difference (X1-X2)
- The determination of the critical value of t is based on the degrees of freedom of two samples (df1+df2)
- The sample scores for comparing distributions are based on the difference between two scores
独立样本的t统计量.txt · 最后更改: 2024/03/22 04:29 由 2104龚文滕