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Statistical Inference
UA DATALAB WORKSHOP SERIES, FALL 2023
📓 These notes: https://github.com/ua-datalab/Workshops/wiki/Statistical-Inference/
💻 Codebase with examples:
📹 Link to Zoom recording - (10-17-2023)
Statistical inference in data science refers to the process of drawing conclusions or making predictions about a population based on a sample of data. It plays a crucial role in extracting meaningful insights from data.
Here are key points to understand about statistical inference:
Sample to Population: Statistical inference involves extrapolating insights from a sample dataset to make statements about the larger population it represents. It addresses questions like, "What can we infer about the entire population based on this sample?"
Types of Statistical Inference: There are two main types of statistical inference:
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Descriptive Statistics: This aspect involves summarizing and describing data using measures like mean, median, and standard deviation, but it doesn't make predictions or inferences about the population.
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Inferential Statistics: This includes hypothesis testing, confidence intervals, regression analysis, and more. It aims to make predictions, test hypotheses, and draw conclusions about the population.
Procedure: Statistical inference typically involves several steps, including data collection, hypothesis formulation, statistical analysis, and drawing conclusions.
Importance in Data Science: Statistical inference is fundamental in data science as it allows data scientists to make data-driven decisions, test hypotheses, and assess the significance of results.
Statistical inference is a cornerstone of data science, enabling professionals to extract meaningful insights and make informed choices from data.
Created: 10-04-2023; Updated: 10-04-2023
Carlos Lizárraga, Data Lab, Data Science Institute, University of Arizona, 2023.
