A request for some basic help with Chi-squared testing:

Say we have two different Likert samples (strongly agree, agree, neutral, disagree, strongly disagree) that I want to do a test fit on. The first sample has a population of 10, its expected frequency is (1,4,2,2,1) and the observed is (2,3,2,2,1). The second sample has a population of 100, its expected frequency is (10,40,20,20,10) and the observed is (20,30,20,20,10), or simply ten times sample one. My significance level is 0.1, and it looks like I have 4 degrees of freedom.

So, question 1 is: using the formula for goodness of fit as (sum of ((O-E)^2)/E), why does sample 1 return a chi-squared of 1.25, and sample two 12.5? It looks like each sample has the same distribution relative to the expected, just one has a greater population. Yet, looking at my critical chi-squared values, one is greatly different than the other. I don't understand why. Can someone explain to me why these apparently similar distributions give different results? Using my critical chi-squared, I can keep Ho for sample one, but must reject for sample two.

Question 2 is the significance factor. I have chosen 0.1 and I think that I must reject Ho (the observed "fits" the expected) if my chi-squared value is less than the critical. So for my example above, I must reject Ho for the 100 sample case with a signifcance level of 0.1, but if I change my significance level to 0.01, then I can keep Ho. I thought that as I make the signifcance level smaller, I am more sure that the fit of observed to expected in in fact "a match". Yet, in this example, I can keep the Ho at 0.01, but must reject at 0.1. How have I misinterpreted the meaning of the significance factor?

Thanks in advance, E.