2 independent sample Hypothesis T test for mean and CIs

Visualize 2 independent samples

2 Sample T-Test and 2 Samp T-Int

In the Visualizer, copy and paste the first 1. example data set (starts with 81.39) and the second 2. (starts with 196.54).
Does each look normalish?:
But even if they didn't, the sample sizes are >30 so good-to-go with the test.
Do they look like they are from 1 or from 2 populations:
sorta obvious, probably don't even need a test to confirm the obvious... but we're learning about the test. Almost complete separation.

Paste the data or their statatistics into the 2-Samp T-Test / 2 Samp T-Int webpage.
H0:μ1=μ2    Ha:μ1≠μ2    "Ha ≠ H0" Two-tailed.
t crit α=.05 crit α=.01 p value Reject H0?   CL        E              CI [           CI ]        has 0? Reject H0?
 
95%
99%

Conclusion: the two samples are likely from population(s).

Now do the first 1. and third 3. data set (starts with 90.40).
Does each look normalish?:
Do they look like they are from 1 or from 2 populations:
sorta obvious, probably don't even need a test to confirm the obvious... but we're learning about the test. This is the opposite extreme of the previous test. Almost complete overlap.

Paste the data or their statatistics into the 2-Samp T-Test / 2 Samp T-Int webpage.
H0:μ1=μ2    Ha:μ1≠μ2    "Ha ≠ H0" Two-tailed.
t crit α=.05 crit α=.01 p value Reject H0?   CL        E              CI [           CI ]        has 0? Reject H0?
 
95%
99%

Conclusion: the two samples likely from population(s).

Now do the first 1. and fourth 4. data set (starts with 122.48).
Does each look normalish?:
Do they look like they are from 1 or from 2 populations:
There's some overlap...

Paste the data or their statatistics into the 2-Samp T-Test / 2 Samp T-Int webpage.
H0:μ1=μ2    Ha:μ1≠μ2    "Ha ≠ H0" Two-tailed.
t crit α=.05 crit α=.01 p value Reject H0?   CL        E              CI [           CI ]        has 0? Reject H0?
 
95%
99%

Conclusion: the two samples likely from population(s).

Now do the first 1. and fifth 5. data set (starts with 109.05).
Does each look normalish?:
Do they look like they are from 1 or from 2 populations:
Looks like X2 is completely "inside" X1. with a SD half that of X1
The means are separated (signficantly? that's what the test will tell us).

Paste the data or their statatistics into the 2-Samp T-Test / 2 Samp T-Int webpage.
H0:μ1=μ2    Ha:μ1≠μ2    "Ha ≠ H0" Two-tailed.
t crit α=.05 crit α=.01 p value Reject H0?   CL        E              CI [           CI ]        has 0? Reject H0?
 
95%
99%

Conclusion: the two samples likely from population(s).
The means are separated significantly.

Now do the first 1. and sixth 6. data set (starts with 99.42).
Does each look normalish?:
Do they look like they are from 1 or from 2 populations:
Looks like X2 is completely "inside" X1. with a SD half that of X1
With essentially same means. So does the big SD difference make a difference for this test?

Paste the data or their statatistics into the 2-Samp T-Test / 2 Samp T-Int webpage.
H0:μ1=μ2    Ha:μ1≠μ2    "Ha ≠ H0" Two-tailed.
t crit α=.05 crit α=.01 p value Reject H0?   CL        E              CI [           CI ]        has 0? Reject H0?
 
95%
99%

Conclusion: the two samples are likely from population(s).
The big SD difference made no difference.