#### Announcements

• Homework due Thursday
• Office hour at 1pm today

• Testing for differences between groups
• Relation to intervals
• Testing for independence of variables

### Differences between groups

• Write $$\bar X_1$$ and $$\bar X_2$$ for the sample means in group 1 and 2, respectively.
• Same for $$S_1, S_2$$ (sample sd) and $$n_1, n_2$$ (group sizes)
• True (unknown) group means $$\mu_1, \mu_2$$
• Instead of testing whether $$\mu_1$$ equals a specific value, we are now interested in testing
• $$H_0 : \mu_1 = \mu_2$$
• Alternative hypotheses: two sided $$\mu_1 \neq \mu_2$$, greater $$\mu_1 > \mu_2$$, less $$\mu_1 < \mu_2$$

• So far nothing special, right? But there are possible complications:
• Do we assume they have the same variance or not? (In general, no)
• Are the samples are “paired” or not? (We’ll see an example in the HTLab)
• In general, check documentation of whatever program you are using and/or wikipedia

• One example: don’t assume equal variances, unpaired

$(\bar X_1 - \bar X_2) / \sqrt{\frac{S_1^2}{n_1} + \frac{S_2^2}{n_2}}$ - This has a $$t$$-distribution with degrees of freedom given by a complicated formula - The equal variances/paired cases have slightly different formulas - Don’t worry, software takes care of details pretty much automatically - Does the formula make sense? If the groups have the same mean, the observed value should be close to 0

### Relation to intervals

• Recall from last time:
• Calculate a 95% confidence interval (possibly one-sided)
• If this interval does not contain the value of the parameter specified by the null hypothesis, then reject the null
• Otherwise fail to reject the null

• For multiple groups:
• If intervals do not overlap: a test for difference would reject the null
• If intervals do overlap: inconclusive
• The examples of published studies we saw where they did not plot confidence intervals may have avoided plotting them for this reason. They conducted tests, and their tests did reject the null hypothesis that both groups had equal means

• It’s also possible to calculate one confidence interval for the difference between the means. If this interval contains 0, fail to reject the null.
• This interval is given by default when using R functions like t.test on two groups

## Testing for independence

• Categorical data: common scenario that isn’t covered by previous examples
• Summarize by calculating a table of counts
nrow(survey) # survey of this many students
## [1] 237
counts <- table(survey$Exer, survey$Sex)
counts
##
##        Female Male
##   Freq     49   65
##   None     11   13
##   Some     58   40
• Most common test in this scenario is called the $$\chi^2$$ (chi-squared) test
• The null hypothesis is that the two categorical variables are independent
• If the null hypothesis is true, would expect numbers in each “cell” of the table to be similar between the groups
• e.g. there are 114 people who exercise frequently, and an equal number of female and male survey respondents. So the expected numbers for the first row are 114/2 = 57
• The test statistic is the sum, over every cell in the table, of $$(\text{observed} - \text{expected})^2/\text{expected}$$
• e.g. this sum over the first row would be $$(49-57)^2/57 + (65-57)^2/57$$
• The observed value of the test statistic is then compared to a $$\chi^2$$ distribution with (nrow-1)*(ncol-1) degrees of freedom
• What are the degrees of freedom for our case?
chisq.test(counts)
##
##  Pearson's Chi-squared test
##
## data:  counts
## X-squared = 5.7184, df = 2, p-value = 0.05731