ABSTRACT
This chapter covers the core principles of statistical inference, focusing on how researchers draw conclusions about populations based on sample data. It begins by distinguishing populations and samples, emphasizing the importance of representativeness through proper sampling techniques. The concept of sampling distributions and standard error is introduced to explain the variability expected in sample estimates. Estimation is discussed via confidence intervals as a way to quantify uncertainty. The chapter then walks through hypothesis formulation (null and alternative hypotheses) and the logic of hypothesis testing using p-values and significance levels. Key topics include testing relationships with correlation and regression, understanding Type I and Type II errors, and ensuring assumptions (like normality and independent observations) are met. Throughout the chapter addresses common misconceptions (e.g., what p-values really indicate) and highlights careful interpretation to avoid overstatement of findings.
