Sampling of Variable (Small Samples) in Machine Learning

In practical problems, we cannot always have a large sample on account of economical factors, therefore we have to depend on a small sample (<30). Since the samples are small we cannot assume as in the case of large sample that the random sampling distribution of a parameter is approximately normal. Secondly, the estimates of parameters of the population made from a small sample are not reliable.