I think you can use a t-test for this, so long as you can consider the inflection point as independent of your other parameters. If the parameters aren't independent (from your description I would guess this is more likely), you can also consider using a Hotelling's $T^2$ test to test whether your first set of parameters is different from the second set. The Hotelling's $T^2$ test is the multivariate generalization of the t-test.
The important thing to think about is what it means to be "significant". How different do these parameters have to be for them to really physically be different in your situation?
The solution is a simple google away: http://en.wikipedia.org/wiki/Statistical_hypothesis_testing
So you would like to test the following null hypothesis against the given alternative
$H_0:p_1=p_2$ versus $H_A:p_1\neq p_2$
So you just need to calculate the test statistic which is
$$z=\frac{\hat p_1-\hat p_2}{\sqrt{\hat p(1-\hat p)\left(\frac{1}{n_1}+\frac{1}{n_2}\right)}}$$
where $\hat p=\frac{n_1\hat p_1+n_2\hat p_2}{n_1+n_2}$.
So now, in your problem, $\hat p_1=.634$, $\hat p_2=.612$, $n_1=2455$ and $n_2=2730.$
Once you calculate the test statistic, you just need to calculate the corresponding critical region value to compare your test statistic too. For example, if you are testing this hypothesis at the 95% confidence level then you need to compare the absolute value of your test statistic against the critical region value of $z_{\alpha/2}=1.96$ (for this two tailed test).
Now, if $|z|>z_{\alpha/2}$ then you may reject the null hypothesis, otherwise you must fail to reject the null hypothesis.
Well this solution works for the case when you are comparing two groups, but it does not generalize to the case where you want to compare 3 groups.
You could however use a Chi Squared test to test if all three groups have equal proportions as suggested by @Eric in his comment above: " Does this question help? stats.stackexchange.com/questions/25299/ … – Eric"
Best Answer
Based on the additional comment you gave to @whuber, it seems that you are looking for an extension of t-test/ANOVA type methods to situations where you have a vector of outcomes per individual (i.e. multiple outcomes per individual, and in particular in the wind vector example, you have a vector with two components for each individual observation ). If interest lies in comparing whether a vector of means from one sample is different from a vector of means from another sample, you can use MANOVA which assumes multivariate normality for the outcomes. Further details on assumptions, mathematical formulation, examples and interpretation can be found here: https://onlinecourses.science.psu.edu/stat505/node/162