statistics help

Hello, i am really struggling to figure out what statistical tests to do on my dissertation data.
my data consists of two groups which total only 34 participants. each group has many factors on a continuous scale measured about them and are all normally distributed.
i've done a multiple regression which came out with no significance. However, i wanted to expand the tests conducted. any ideas of how i can test my data?

If you tell us what the scientific question is that you're trying to answer with your data, we may be able to help you!

I'm trying to see if there is a relationship between the hatching success (%) of nests and the parameters of the nest e.g temp, distance to sea, depth..

Let me see if I've understood your problem correctly. You have data on 34 nests that records the percentage of eggs hatching per nest. Do you have the data recorded only in the form of a percentage, or do you have it recorded as "m out of n eggs hatched in this nest"? If you only have the former, then linear regression is probably the tool you should be using; if you have the latter, it would be better to use a binomial generalized linear model (i.e. the generalization of logistic regression to binomial outcomes).

Next question is: how many parameters do you have recorded for each nest? For a sample size of 34, regression models will only support 2, maybe 3 at a stretch, parameters as covariates.

If you have many more than this then we're going to have to get crafty.

Technique A: For each nest parameter in turn, do a univariate regression. Select the two or three parameters that have the largest effect size that is also statistically significant (i.e. don't choose on the smallest p-value, but on the largest effect size that has p < 0.05). Stick these two or three parameters into your regression.

Technique B: Select those two or three parameters that the scientific literature suggest should be associated with the outcome into your regression model.

Technique C: enter all of your parameters into a LASSO regression and let it find the two or three parameters that survive shrinkage the longest. Put these two or three parameters into your regression equation. (This is a technique called the "relaxed lasso".

For your situation, with a small sample size, B is the best. C is next best, but is using "advanced" techniques that you might need to get assistance with. A is often used (badly) and will result in very imprecise p-values for the regression coefficients.