What statistic test to use? I am new to statistics!
I have 2 groups of city population based on education level (group A: educated, group B: without education)
I try to figure out if there is a difference between the number of people who live in the city, based on education
What statistic tests to use to prove the following hypothesis, or what steps to follow?
I already calculated mean and standard deviation for both groups, and I also know N for both, but I am stuck from here, what test to use.
H0: no difference between education level
Ha: more "educated" people than "without education" in the city
Ha2: more "without education" people than "educated" people in the city
Also, is it possible to have 2 alternative hypothesis, or I should think to state the Ha as a single hypothesis? How can I make it into 1 alternative hypothesis?
Thanks!

 A: Usually, the t-test comes into play for this (check its assumptions). Also, have a look at a general procedure.
A: Welcome to the world of statistics !
As a beginner myself, I always found references on "how to select the good statistical test" useful.
This paper by might be a good place to start understanding your data and how to properly analyze the results. You can follow the step by step tree to select the most appropriate method.
Hope it helps !
A: Regarding the part about the two hypotheses:
Normally you just report the effect that you measured. For instance:

*

*An estimate for the number of educated people in the city is 60%, this is more than half the population.

The statistical test, e.g. a t-test, is performed in order to find out whether the null hypothesis is correct or not and to characterise the significance of the estimated effect size. The test is used to give an indication of the probability of a false positive result. If the p-value is high, then this means that the measured effect (e.g. that 60%) is not a strong evidence against the null hypothesis (since a deviation of this size could likely happen even when the null hypothesis is true of the p-value is high).

The idea about a one-sided t-test is to adjust the power of the test and this is useful when you are more interested in effects of a particular direction. (btw I write t-test but you might consider a different test, e.g. a test for a binomial proportion)
