Computational analysis is the process of taking a sample and finding patterns in the data. This can be done by taking small samples of data from a larger sample, where the information will be more statistically significant or by analyzing smaller subsets of data to produce statistical significance. There are many uses for statistical significance in the natural sciences.
What is computational science?
The best way to study data in a statistical context is by using data mining. It is a very common and widely used method, as well as one of the most powerful. Data mining requires that you have access to all types of data – from the most general type, like a survey, to a much more precise type, such as the results from a scientific experiment performed in the laboratory. You need to be able to sort out the information, so that you can use it to produce meaningful conclusions. One example of a type of data mine is a Google Trends search, for example.
Another analytical method is Bayesian statistics, which works by examining the results from a number of experiments or samples and using the information in order to formulate an interpretation of the data. One example is a study that looked at the link between cigarette smoking and asthma. This particular study found a strong, statistically significant correlation between smoking and the likelihood of developing asthmatic symptoms.