Before **inferential statistics**, we described descriptive statistics in a separate article and explained some of the methods used. In this article, we are going to provide explanations about inferential statistics.

Let’s start with the question, “If you are going to examine the behavior of the people of a large country, can you test each individual in that country and look at the conclusion?” Obviously, even if this is possible, it is not affordable because it requires a lot of time and money. Here, the main point in the discussion is **inferential statistics**, which includes more advanced methods than descriptive statistics.

**inferential statistics**.

**inferential statistics**, we should examine a smaller set of the statistical population and generalize the result to the statistical population. In fact, the statistical population is the population on which the surveys are supposed to be conducted. **Sampling **in the statistical population is done in different ways, some of which are: random, stratified and quota **sampling**.

In descriptive statistics, symbols are Latin and in inferential statistics, symbols are Greek.

__inferential statistics__ is **sampling**. As we mentioned earlier, we have three categories for sampling:

1- Probability

- random
- clustered
- Systematic
- Stratified

2- Non-probability

- judgmental
- snowball
- Availability

3- Oblique and non-oblique

In the following article, we introduce various types of inferential statistics.

**Nonparametric inferential statistics** are used when we have little information about the distribution of the data. In fact, we perform our statistical tests with fewer assumptions. Depending on his or her goal in non-parametric inferential statistics, the researcher can use different methods, some of which include variance analysis, covariance analysis, correlation, etc.

**Bigpro1** website, you can easily analyze your datasets and extract useful information from them. Just go to the data mining section in your user profile and then click on the statistical analysis option. In the next step, it is enough to select one of your datasets and choose one of the **statistical analysis methods depending **on your purpose. In bigpro1, it is possible to use non-parametric tests.

**inferential statistics**, we are aware of the condition of the data in the statistical population, and these tests are mostly used for quantitative data, which usually have a normal distribution. When we have information about the shape of the data distribution, parametric correlations are more preferable than parametric tests.

In the accuracy test, you can evaluate the accuracy of your model when using binary data (data that has two states) for correct values and obtain different measures of the convolution matrix. Even if you don’t have a specific threshold limit, you can still choose one of them by using the roc curve chart and obtaining three values of the upper, lower and middle threshold limits and consider it as the threshold limit.

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