Just because the results look good, doesn’t mean they are statistically significant. A hypothesis test tries to determine whether, statistically, one can expect the same results from a repeated study. Whether the null hypothesis will be rejected or not, will depend on the estimated variance in your experiment, the size of the difference in proportion as well as the number of samples used.
The null hypothesis states the results obtained are purely random, whereas the alternative hypothesis states that the results are extraordinary. That is, at a 95 percent confidence level ($\alpha=5$ percent), the null hypothesis will only be rejected if results is expected to be ''that good'' or better in just 5 out of 100 cases. That is, the null hypothesis is rejected in favour of the one-tailed alternative hypothesis is if $p(t \ge t_d | H_0)\le \alpha$ where $t_d$ is your test statistic.
This can also be interpreted as, the zero hypothesis will correctly rejected the null hypothesis 95 percent of the times. Incorrectly rejecting the null hypothesis is called a type I error. The probability of a Type I error = $\alpha$.