I like user3923510's answer, there's just something I wanted to add based off your comments.
The goal of statistics is not to verify your hypothesis. The goal of statistics is to verify what the data is saying. If we think of simple classical statistics, we might perform an experiment, visualise the data, and then run an analysis to verify whether patterns observed in the visualisation are significantly different or not.
Positive results are not the only worthwhile results. Negative results are also telling you something about your data. Since you haven't provided details about your data, let me come up with something.
Say there were two rivers. I hypothesise that the average weight of trout is going to be higher in river 1 because I know that river 1 is generally warmer than river 2. So I go and collect fish from each river and weigh them. I then perform a t-test to see whether average weight is significantly different, and I get a p-value of 0.085. Therefore the average weights of fish between the two rivers is the same.
This is my result though!! I do not now try to tweak the data to prove my original hypothesis. The statistics have done their job, and I have learn't something in the process. The weights do not differ! This is an interesting result and I will now design follow up experiments to investigate further.
Trying to force significant results is dangerous. You have your results. Trust them, use them, figure out what they mean.