Data Mining in Statistics deals with finding useful patterns in data sets. This includes hypothesis testing and parameter estimation.
Hypothesis testing is a part of inferential statistics, which starts with a initial premise (called the Null Hypothesis) and then data collected is tested with this premise. If the hypothesis is validated for the data to a certain degree, then the Null Hypothesis is said to be True or else it is said to be False.
Parameter Estimation deals with finding parameter, like means, standard deviations, etc. that would describe the distribution of a given sample of data points.
Finding optimal strategies for Data Collection is another issue with Statistics. Methods need to be developed, which would efficiently search large databases to find representative sample data points.
Different Data Mining techniques have to utilized for evolving data as opposed to for static data.
Model Estimation of the data samples is also an important aspect of Statistics. Samples can have different model distributions, leading to development of different algorithms for them. Applicability of algorithms becomes a major issue in this case.