I have two sets of data that hypothesis tests have shown to be normal and from the same distribution. I'm using MATLAB and for the way they give p-values, higher p-values suggest a better goodness-of-fit, as I understand it. I want to combine the two data sets to get the parameters for the combined data set. However, when I run a hypothesis test on the combined data set, hypothesis tests either reject the null assumption of Normality or come very close as seen by the test statistics. I've been looking for an explanation but have no idea where to start. Could anyone shed some light into this?
If you're talking about a test of the null hypothesis that your sample data come from an exactly normal distribution, you'll probably want to see this question: Is normality testing 'essentially useless'?
Most (if not all) real data is not normally distributed exactly. You can estimate the skewness and kurtosis to quantify how different your data's distribution is from a normal distribution. Even small amounts of skew or excess kurtosis become easier to estimate precisely as data aggregates. By combining your two samples, you're improving the precision of your estimates. It seems you have enough data to estimate your distribution's differences from a normal distribution so precisely that, with your normality tests' $p$ values, you can say there's "very close" to or less than $\alpha$ (typically 5%) chance that, from a truly normal distribution, you would randomly sample a dataset that is as large and at least as different from a normal distribution as yours is. Since that's the null hypothesis in a nutshell, and it seems very unlikely to be true, you're probably right to reject it, but this is somewhat of a foregone conclusion.
Whether your data are too different from a normal distribution for your intended purposes is another question. The hypothesis tests you're using probably can't answer it for you by themselves: their outcomes will depend on your sample size, but won't take into account the sensitivity of whatever other analyses you have planned, among other reasons. You would probably get a better idea of how important this issue is by studying the sensitivity of your planned analyses to violations of the normality assumption; some really aren't very sensitive to the usual kinds of minor violations that are present in approximately normal data. You might get better sense of how non-normal your data are from a Q-Q plot, as @Glen_b suggested; knowing just how much non-normality is too much can still be difficult to tell from these plots though.
If you have time, or if your data are particularly problematic, you might want to look into robust alternatives to any other analyses you plan to run. This might even be one of the shorter routes to avoiding validity problems while ignoring the idiosyncrasies of your distribution, but bear in mind that those idiosyncrasies are sometimes interesting in their own right! Finally, if you're particularly serious about minding the sensitivity of any subsequent analyses to data that aren't from exactly normal distributions, you might want to consider doing some simulation testing of those analyses too.
(Full disclosure: for this answer, I borrowed and modified some text from a previous answer of mine.)
Edit: The null hypothesis of the two-sample Kolmogorov-Smirnov test states that the two samples are drawn from populations with the exact same distribution (or are both drawn from the same population, which is roughly equivalent). This would mean equal distributional parameters too...but to test normality (as you say you have), one of the distributions (your "reference distribution") would have to be a normal distribution. You'd need to run two such tests to compare each of two distributions to a normal distribution, or to each other and then one of them to a normal distribution. Testing twice inflates your overall false rejection rate, so you would want to adjust for multiple tests before rejecting those nulls (which you haven't anyway, so this is somewhat moot). Wikipedia mentions a multivariate version of this test, but it's relatively new, so I doubt you would've performed this version of the test unknowingly.
Your interpretation of the $p$-value suggests a fundamental misunderstanding about the nature of your hypothesis test. A test cannot ever confirm that the null is true. At best, it can only state that there is insufficient evidence to suggest that the alternative hypothesis is true.
If you get a "high" $p$-value using a goodness-of-fit test or a test of normality, that doesn't mean the evidence is strong that the data are drawn from the same distribution, because the $p$-value is a conditional probability: it is the probability of observing a result as extreme as that obtained, given $H_0$ is true. So, at best, all your test has been able to show is that the data you observed does not suggest any reason to believe they are drawn from different distributions--this is not the same as saying they are in fact drawn from the same distribution.
This subtlety becomes all the more apparent when we consider that the nature of the tests you are using here tend to have very low power: that is, even if the samples are drawn from different distributions, you would need a LOT of observations for the test to affirmatively conclude that they are.
Combining two normally distributed dataset into one, can result in a not-normally distributed dataset. This is best illustrated by an example:
(supposing you're working with R)
# creat two normal distributed datasets x <- data.frame(var=rnorm(5000,2,1)) # 5000 observations, mean = 2, sd = 1 y <- data.frame(var=rnorm(5000,6,1)) # 5000 observations, mean = 6, sd = 1
When you make density plots the look like this for x and y respectively:
Both look rather normally distrubed. Now combine the two datasets.
# combining the datasets z <- rbind(x,y)
When you make a density plot of
z, you can see that it doesn't look like a normal distribution at all: