I would conduct the following hypothesis test and show that the there is no statistical evidence to suggest the proportion is less than 18%
Hypothesis Test for proportions:
Let X be the number of success in n independent and identically distributed Bernoulli trials, i.e., X ~ Binomial(n, p)
To test the null hypothesis of the form
H0: p = p0, or
H0: p ≥ p0, or
H0: p ≤ p0
Assuming that n*p0 > 10 and n * (1-p0) > 10 (some will say the necessary condition here is > 5, I prefer this more conservative assumption so that the approximations in the tail of the distribution are more accurate) then
find the test statistic z = (pHat - p0) / sqrt(p0 * (1-p0) / n)
where pHat = X / n
The p-value of the test is the area under the normal curve that is in agreement with the alternate hypothesis.
H1: p ≠ p0; p-value is the area in the tails greater than |z|
H1: p < p0; p-value is the area to the left of z
H1: p > p0; p-value is the area to the right of z
If the p-value is less than or equal to the significance level α, i.e., p-value ≤ α, then we reject the null hypothesis and conclude the alternate hypothesis is true. If the p-value is greater than the significance level, i.e., p-value > α, then we fail to reject the null hypothesis and conclude that the null is plausible. Note that we can conclude the alternate is true, but we cannot conclude the null is true, only that it is plausible.
The hypothesis test in this question is:
H0: p ≥ 0.18 vs. H1: p < 0.18
The test statistic is:
z = ( 0.16 - 0.18 ) / ( √ ( 0.18 * (1 - 0.18 ) / 200 )
z = -0.7362102
The p-value = P( Z < z )
= P( Z < -0.7362102 )
= 0.2308014
Since the p-value is greater than the significance level of 0.05 we fail to reject the null hypothesis and conclude p ≥ 0.18 is plausible.