Lecture 8B: Comparing Means T-Test
Purpose
To compare means between 2 groups
u2-u1= 0
Overlapping confidence intervals: population mean is not significantly difference
Different in sample
T-Test
It is right because of the Cumbersome method whereby you calculate several CI
T-distribution: looks like a normal distribution so long as you have a wide range of data
Test on 2 group means: is the difference 0 or not
Test whether difference beween group means is statistically different
2 different groups: repeated test for the same observation, and 2 test for different tes
Formula: check lecture slides
Independent sample: ask one group of people independent from the other group of peeps
Dependent: asking the same peeps
Dependent sampes share characteristics that affect the variace
Dependent sample formula: check lecture slide
Independent sample formula: check lecture slide
Dependent T test assumptions:
Variables measured at interval/ratio level
Sampling distribution is normally distrubted
Parametric test
N>30, variables not clearly non-normally distrubuted
Independent T-test assumptions:
Variables measured at interval/ratio level
Sampling distribution is normally distrubted
Parametric test
N>30, variables not clearly non-normally distrubuted
Homegeneity of Variance: variances of both samples are the same
Levene's Test
Independent T-test: variances assumed equal
H0= variances equal
Outcome Levene's test: not significant means variances equal
Sig. > 0.5
Look up on SPSS and hull hyothesis is true
Significant: variances are not equal Sig < 0.05
Look down on SPSS and null hypothesis is false
Effect Size
Cohen's d for dependent (paired sample)
You compare the difference to standard deviation of difference
0.2 is small and 0.5 is medium and 0.8 is large
Big: it is significant
Small: it is not significant
Independent : which S do you pick, you pick a control group variance (as a baseline)
Pooled variance: variance of one group and another group