Table of Contents

**What is Descriptive Statistics?**

Descriptive statistics is a branch of applied mathematics that deals with data organization, analysis, and interpretation. It describes the characteristics of a given population or data set and involves the description, tabulation, and graphical representation of data.

These summary statistics are used to describe the data and can include measures of central tendency (such as the mean and median) and variability (such as the standard deviation and range).

Descriptive statistics can also describe relationships between two or more variables, the distribution of a data set by creating a probability distribution, outliers, or exceptional values in a data set by creating a box plot that shows the range of values in the data set and any outliers.

Descriptive statistics identify data trends by creating a time series that shows how the values in the data set change over time, the characteristics of a given population or data set, and involves the description, tabulation, and graphical representation of data. It is often used to uncover meaningful patterns and relationships in data.

**What is Inferential Statistics?**

Inferential Statistics is a branch of statistics used to make inferences and predictions about a population or group of data based on a sample of that population or group. The most common types of inferential statistics are hypothesis testing, correlation, and regression.

It uses descriptive statistics, such as measures of central tendency, to conclude a sample of the entire population. This type of statistics is widely used in research, business, and science, mainly when a population is too large or complex to observe or measure.

Inferential statistics are based on probability theory and involve using a sample of data to make conclusions and predictions about the population, used to draw meaningful conclusions from a large set of data, test hypotheses, and make predictions about a population or a group of data.

Inferential statistics is an essential tool for understanding and interpreting data. It is used in research to conclude a population or group of data, in business, to make predictions about customer behavior or sales trends, and in science, to predict the behavior of particles or phenomena.

Inferential statistics is essential to data analysis, as it helps researchers draw meaningful conclusions and predict the population or group of data they are studying. Using the appropriate statistical methods to draw meaningful conclusions and make accurate predictions is essential. By understanding the basics of inferential statistics, researchers can better analyze and interpret data and make more informed decisions.

**Difference Between Descriptive and Inferential Statistics**

- Descriptive statistics describe the data in terms of its measures of central tendency, while inferential statistics are used to make inferences about a population from a sample.
- Descriptive statistics uses simple numerical summaries, while inferential statistics use more complex models and techniques.
- Descriptive statistics describe the data, while inferential statistics involves making inferences about the data.
- Descriptive statistics are used to describe the data, while inferential statistics are used to explain the data.
- Descriptive statistics provide a snapshot of the data, while inferential statistics.

**Comparison Between Descriptive and Inferential Statistics**

Parameters of Comparison | Descriptive Statistics | Inferential Statistics |

Focus | Focuses on the summary of the data | Focuses on making inferences from the data |

Work | Summarizing the data | Make predictions about future data |

Form of the final result | Chart, graphs, and tables | Probability |

Usage | Used to describe the characteristics of a data set | Used to conclude about a population based on sample data |

Function | Make estimates, test hypotheses, and draw conclusions | Describes the main characteristics of a data set |