Industry
general
Skills
approach
data-understanding
Tools
python
Learning Objectives
Learn the basics of inferential statistics, including hypothesis formulation, significance testing, and p-value interpretation.
Differentiate between one-tailed vs. two-tailed tests and one-sample vs. two-sample tests to ensure the correct approach to hypothesis testing.
Recognize when to use Z-tests or T-tests based on sample size and data characteristics to validate hypotheses effectively.
Understand the use of ANOVA in comparing means across multiple groups, enabling more complex insights.
Learn to analyze relationships between categorical variables using the Chi-Square test.
Apply learned techniques in real-world scenarios to validate business insights with statistical confidence.
Overview
Prerequisites
- Basic knowledge of statistics, including mean, median, mode, and standard deviation.
- Familiarity with concepts of probability and distributions (normal distribution in particular).
- Some experience with data analysis and interest in business applications of statistical methods.
- Basic understanding of data types and when to use categorical vs. numerical analysis techniques.