## 08 Oct Descriptive vs Inferential Statistics

Descriptive and Inferential Statistics are the two types of statistics. In Descriptive statistics, the observations are summarised from a set of data. It deals with the summary and analysis of data. Inferential statistics is about making predictions or inferences about a population based on a sample of data. Let us see the differences between them with examples:

Descriptive Statistics | Inferential Statistics | |
---|---|---|

What | Summarize and describe the main features of a dataset | Make inferences and predictions about a population based on a sample |

Data | Uses the entire dataset | Uses a data from small sample to make predictions about the larger population |

Limitations | Describes only the data you have, cannot make predictions or generalizations | Dependent on the representativeness of the sample, susceptible to sampling errors and biases |

Output | Displays the data in form of bar charts, scatter plots, histogram, etc. | Statistical inferences, predictions, estimates, p-values, confidence |

Examples | Mean, median, mode, standard deviation, variance, range, percentiles | Hypothesis testing, confidence intervals, regression analysis, etc. |

Applications | Data summarization, visualization, data cleaning, identifying patterns and outliers | Model building, hypothesis testing, sampling, probability estimation, etc. |

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**Read More**:

- What is Machine Learning
- What is a Machine Learning Model
- Types of Machine Learning
- Supervised vs Unsupervised vs Reinforcement Machine Learning
- What is Deep Learning
- Feedforward Neural Networks (FNN)
- Convolutional Neural Network (CNN)
- Recurrent Neural Networks (RNN)
- Long short-term memory (LSTM)
- Generative Adversarial Networks (GANs)

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