In general, t

**he statistics can be interpreted as a way to obtain information from the data**. In more detail, statistical significance can be grouped into three, namely:- Statistical reporting is defined as a set of data, for example, football statistics, population statistics, and so on.
- The statistics are calculated quantity of a set of data, for example: proportion, average and so on.
- Statistics also be interpreted as a discipline and art of making inference of a specific unit to something general.

**Data is something that is considered to provide a snapshot of a situation or problem. The data is considered as something that is not necessarily true, but in practice the assumption or assumptions are often used as a basis for decision making, such as the government considers sufficient supply of rice stocks as data showed an increase in rice production, it was decided not to import rice.**Therefore, an assumption or assumption is not necessarily true, then when it is used as a basis for decision-making, the decision could still be mistaken or wrong. Thus the statistical assumption that the hypothesis should be tested first.

Talk statistically meaningful sample speech. The sample is part of members of the population who were subjected to experiments. The population is a set of objects is complete and clear that wants to learn its properties. Activities to examine all objects (population) called census activities, eg population census, census of agriculture, etc. Activities examined part of the population which is the object selected is called the survey. Descriptive measure of a population is a parameter, while the size of a sample is descriptive statistics. So while the parameters of the population has had a statistical sample. Census data can be analyzed in a descriptive way. Survey data can be analyzed by means of descriptive and inferential. Inferential is a form of decision-making which includes the statement, explanation, comparison, estimates, projections, etc.

Statistical methods can be classified into two, namely statistical parametric and non-parametric statistics. Parametric testing is a way to test the hypothesis that is based on several assumptions:

- The observation of the sample should be selected from the population who are considered to have a normal distribution.
- In the case of two different test parameters or more, the populations are not only considered to have a normal distribution but also have the same variance (assuming
**homoskedastisitas**).

The validity of these assumptions determine the extent of the parametric test results mean or not. While nonparametric method never formulate assumptions about the population from which the sample is selected. The statistical methods used in nonparametric statistics is related to the data in the form of ranking or qualitative data (nominal or ordinal scale) or quantitative data were not normally distributed. Therefore,

**nonparametric statistics are often referred to as the free statistical distribution**. In nonparametric statistics, we will examine the characteristics of the population without the use of specific parameters. Therefore, this test statistic called non-parametric statistics that will test whether different populations location of the test if the average of the different populations.
Be aware that the non-parametric test should not be used if the parametric test can be applied, because the level of efficacy of non-parametric tests is lower than the parametric test. But you as decision makers or researchers do not misinterpret that the degree of usefulness of non-parametric statistical method under parametric statistical methods. Of course not, each method is made with special specifications according to the kinds of data used. Improved efficacy with a non-parametric test should enlarge the sample. But as we all know enlarge the sample mean will add to the cost, time, etc.

An explanation of what and how to use the methods of parametric and nonparametric statistics will be described in another paper sessions. Enjoy statistics.