Two Sample T-test
Two Sample T-test
Introduction
A two sample t hypothesis tests also known as independent t-test is used to analyze the difference between two unknown population means. The Two-sample T-test is used when the two small samples (n< 30) are taken from two different populations and compared.
Assumption
- The data are continuous
- The data are normally distributed in each group.
- The sample is a simple random sample form its population. Each individual in the population has an equal probability of being selected in the sample.
- The variances for the groups are equal.
Hypotheses
The null hypothesis (H0) and alternative hypothesis (H1) of the one sample T test can be expressed as:
H0: µ1 = µ2
H1: µ1 ≠ µ2
where µi is the population mean of population i.
Test Statistic
- Where n1 and n2 are sample sizes
- x̅1 and x̅2 are means of sample sizes
- Sp is the pooled standard deviation
P-value
P-value = Pr(|t_n1+n2-2| > |t|) = 2Pr(t_n1+n2-2 > |t|)
If p-value is less than 0.05 significant level, then the null hypothesis is true, there is a significant different between 2 groups.
If p-value is greater than 0.05 significant level, then the null hypothesis should be rejected, there is no significant different between 2 groups.
Relation with Linear Regression
Y = b_0 + b1x + e
Then,
H_0 : µ1 = µ2 <=> H_0 : b_1 = 0
H_1 : µ1 ≠ µ2 <=> H_1 : b_0 ≠ 0
R code
(from STA305 assignment)
Reference


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ReplyDeleteI saw many comment asking about the difference between one sample and two sample t-test. Here is what I got:
ReplyDeleteThe 2-sample t-test takes your sample data from two groups and boils it down to the t-value. The process is very similar to the 1-sample t-test, and you can still use the analogy of the signal-to-noise ratio. Unlike the paired t-test, the 2-sample t-test requires independent groups for each sample.