Statistical Inference And Hypothesis Testing

This section covers the concepts of statistical inference and hypothesis testing, allowing data scientists to make reliable decisions based on sample data.

What Is Statistical Inference?

Statistical inference allows for the estimation of population parameters and hypothesis testing based on sample data.

Key Concepts In Hypothesis Testing

This section outlines the fundamental concepts of hypothesis testing, including the null and alternative hypotheses, test statistics, significance levels, p-values, and the potential for Type I and Type II errors.

Steps In Hypothesis Testing

This section outlines the sequential steps necessary for effectively conducting hypothesis testing.

Types Of Statistical Tests

This section discusses the various statistical tests used to analyze data, focusing on their applications based on known and unknown population parameters.

Confidence Intervals

Confidence intervals provide a range of values likely to contain the true population parameter.

Practical Applications In Data Science

This section covers various practical applications of data science, emphasizing the statistical methods utilized in different contexts.

Best Practices

This section outlines essential best practices for conducting statistical inference and hypothesis testing.

Summary

This section summarizes the foundations of statistical inference and hypothesis testing, highlighting their importance in data-driven decision-making.