Eliminating variation in sample size for A/B tests Firstly, I'll apologise for my limited understanding on this topic. 
Let's say I am running an A/B test with a pool of 20,000 prospects and I split them 50/50 into A and B.
The problem is, the number of people seeing my experiment is skewed toward one group.
Group A has ~7.32% seeing the experiment 
Group B has ~6.34%
Out of 10,000 people that gives me a pool of 
A - 732 people
B - 634 people
I'm measuring conversions, so pool A has a conversion rate of 19.8% (~145 people), pool B has a conversion rate of 18% (~114 people)
My instinct says that since pool A has a greater sample size, the result is unreliable. How would I go about eliminating this variation? 
is it a p value thing?
 A: Assuming that conversion rate is not affected by participation rate (sample size), here is Minitab's output for your data.
Test and CI for Two Proportions 

Sample    X    N  Sample p
1       145  732  0.198087
2       114  634  0.179811

Difference = p (1) - p (2)
Estimate for difference:  0.0182767
95% CI for difference:  (-0.0232830, 0.0598364)
Test for difference = 0 (vs ≠ 0):  Z = 0.86  P-Value = 0.389

So as @user11852 has Commented, there is not a significant difference
between the two conversion rates.
Furthermore, a power analysis shows that, for practical purposes, samples of about these sizes would be 
nowhere near sufficient to
detect a real difference in conversion rates of 19.8% compared with 18%. 
Power and Sample Size 

Test for Two Proportions

Testing comparison p = baseline p (versus ≠)
Calculating power for baseline p = 0.18
α = 0.05

              Sample  Target
Comparison p    Size   Power  Actual Power
       0.198    7426    0.80      0.800045
       0.198   12293    0.95      0.950012

The sample size is for each group.

You would need for the group with the smaller sample size to have
about 7500 subjects in order to have power 80% for detecting
such a small difference in conversion rates.

A: Sample sizes between your groups can vary without it causing a problem for inference. As others mentioned, an exception is if what causes group assignment is correlated with outcome. The biggest hit for inference is that you'll have lower experimental power as the ratio between sample sizes differs from being equal.
