This paper discusses and presents maximum likelihood estimators of proportion
for finite population as an alternative to the standard estimator. The properties of the
obtained random sample under simple random sampling are investigated. The likelihood
functions for both with and without replacement sampling schemes are constructed and
the Maximum Likelihood estimators for proportion are derived. The properties of
estimators are also investigated analytically. It was found that, under simple random
sampling without replacement, the Maximum Likelihood estimator is biased with a larger
variance comparing to that of standard estimator, sample proportion. The bias adjusted
estimator may be a potential candidate in estimating population proportion, and may also
lead to more accurate inferences in term of interval estimation or hypothesis testing.
Moreover, the concrete link between theory of sample survey and theory of statistical
inference is elaborated. The discussion given would enable for in depth knowledge in
statistics, i.e. understanding in concepts, principles, and theories of statistics to be
rigorously developed. This, in turn, allows for cognitive skills, one of the keys learning
outcomes in statistics education at tertiary level, be achieved.