# Question 2 (a)

# Question 2 (c)

# Question 3 (c)

# Question 4 (a)

# Question 5

Assumption in 2 sample independence T-test

**Normality**: Assumes that the population distributions are normal. The t-test is quite robust over moderate violations of this assumption. It is especially robust if a two tailed test is used and if the sample sizes are not especially small. Check for normality by creating a histogram.**Independent Observations**: The observations within each treatment condition must be independent.**Equal Variances**: Assume that the population distributions have the same variance. This assumption is quite important (If it is violated, it makes the test’s averaging of the 2 variances meaningless).If it is violated, then use a modification of the t-test procedures as needed.

Paired Sample T test

- The matched-pair t-test (or paired t-test or paired samples
t-test or dependent t-test) is used when the data from the two
groups can be
**presented in pairs**, For example where the same people are being measured in**before-and-after**comparison or when the group is given**two different tests at different times**(e.g. pleasantness of two different types of chocolate).

- The matched-pair t-test (or paired t-test or paired samples
t-test or dependent t-test) is used when the data from the two
groups can be
Assumptions in paired sample t-test

The first assumption in the paired sample t-test is that only the

**matched pair**can be used to perform the paired sample t-test.In the paired sample t-test,

**normal distributions**are assumed.Variance in paired sample t-test: in a paired sample t- test, it is assumed that the

**variance**of two sample is**same**.The data is measurement data-interval/ratio

**Independence**of observation in paired sample t-test: in a paired sample t-test, observations must be independent of each other.

Paired t-test vs two-sample t-test

Paired t- test

# Question 6 (b)

# Question 6 (c)

# Question 6 (d)