When the population variance of a variable is unknown.
What is a consequence of replacing the population standard deviation with a sample standard deviation?
One-sample t-test
It changes the distribution of the test statistic. It is no longer a z-distributed but t-distributed.
What is the relationship between df and t- and z- distribution?
One-sample t-test
When df is small, there is a large difference between the t- and z- distribution. However, when df is large (i.e. greater than 30), there is no noticeable difference.
How does t-critical compare to z-critical?
One-sample t-test
t-critical will always be bigger than z-critical
Effect size
One-sample t-test
amount of difference in SD
What is estimation?
One-sample t-test
Inferential process of using statistics to estimate parameters.
Point Estimate
One-sample t-test
a single number is used to estimate a parameter
-null hypothesis testing
Interval Estimate
One-sample t-test
a range of values is used to estimate a parameter
Confidence Interval
One-sample t-test
when an interval estimate is accompanied by a specific level of confidence (probability)
can be computed for any statistic
Width of Confidence Interval
One-sample t-test
affected by confidence level and standard deviation of a statistic
- confidence level increases, width of confidence interval increases
-standard deviation of a statistic increases, width of confidence interval increases
When is an Independent-samples t-test used?
Independent-samples t-test
used to compare a mean of the DV between two groups
What is the df of independent-samples t-test?
Independent-samples t-test
df = (n1 -1) + (n2 - 1)
= N-2
Pooled Variance
Independent-samples t-test
weighted average of a variances of a DV for group 1 and 2
Assumptions of independent-samples t-test
Indpependent-samples t-test
Normality
No Outliers
Homogeneity of Variance
Independence of Subjects
If violated results/conclusion of t-test could be invalid
Assumption: Normality
Independent-samples t-test
DV should be normally distributed within each group
-tested using the Shapiro-Wilk
Assumption: No Outliers
Independent-samples t-test
an outlier has an undue influence on a statistic
assumption is checked by examining histogram and Q-Q plot
Assumption: Homogeneity of variance
Independent-samples t-test
the variances of the dependent variable are between group 1 and 2, Leven's test of homogeneityof variance will be used to test this assumption
Assumption: Independence of Subjects
Independent-samples t-test
design consideration. one subject outcome is not influenced by another's outcome