Q:
# What Are the Two Types of Statistics?

The two major types of statistics are descriptive statistics and inferential statistics. Each of these statistical segments serves specific purposes, and they are used to accomplish different objectives.

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There are two types of statistics. They are descriptive statistics and inferential statistics. Descriptive statistics describes and summarizes data. Numerical measures, such as average, correlation and standard deviation, describes the different features of a data set. Inferential statistics examines the relationship between a few variables that are in a sample. It then makes predictions or generalizations about how those types of variables relates to a larger population.

This is the type of basic statistics that describes the happenings within a dataset of a population. It is a very important part of the quantitative study of statistics because it is very useful in interpreting data. It allows social scientists to see patterns within data. This allows them to make sense of this data. Descriptive statistics are only used to explain a data set of the population that is being studied. It cannot be extrapolated or generalized to any other population or group. This is one main way that descriptive statistics is different from inferential statistics. Social scientists use two different types of descriptive statistics. These are measures of spread and the measures of central tendency.

**Measures of Spread**

When talking about measures of spread, social scientists show how a data set is distributed and the relationship with this data set with each other. This includes a range, quartiles, mean absolute deviation, variance and frequency distribution. When speaking about measures of spread, they are usually represented visually as in histograms, bar charts and tables. These visual aids assist social sciences with understanding and then interpreting the trends in the data.

**Measures of Central Tendency**

This type of descriptive statistic captures the trends within the data. This trend is generalized and includes expressions such as mode, median and mean. A mode is a common value of data like something that tells us that age when most people get their first job. A median is the middle range of the data. For example, what is the median age when most people buy their first car? When speaking about mean, it tells social scientists about a mathematical average of data. For example, the average age when people get married is an example of a mean.

**Understanding Inferential Statistics**

Inferential statistics look at the relationship between different variables that are in a sample set. As the name implies, it makes predictions and generally generalizations about them and how they will be applied to a greater population. When looking at quantitative social sciences, most applications use inferential statistics. This is because using descriptive statistics to measure everything would be too costly. It is easier to measure a group and extrapolate the research rather than to measure each individual unit by itself.

Since inferential statistics examines the relationship and extrapolates on research, it is important to use statistically valid sample sets when doing this type of quantitative analysis. The different types of inferential statistics include logistic regression analysis and linear regression analysis.

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