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10: Introduction to Inferential Statistics

  • Page ID
    56390
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    Learning Objectives

    By the end of this chapter, you will be able to:

    • Understand the goal of inferential statistics
    • Describe the general linear model

    Key Terms

    • Inferential
    • Patterns
    • Models
    • General Linear Model

    Recap

    In chapters one through four, we were in statistical concepts camp. In that camp, we learned the foundations, the concepts, the “why,” the hallmarks of statistics, the process of analyzing numbers, their variation, and how they represent the phenomenon we observe. Then, in chapters five through nine, we were in descriptive statistics camp. The purpose of descriptive statistics is to describe the numbers and how they represent the variation we see in the phenomenon we see. Describing these numbers means sorting, organizing, and simplifying the numbers in ways that tell us a story about the meaning of the variation.

    In chapters ten and twelve, we venture into inferential statistics. One issue to highlight from the outset is the issue of causality. Statistics cannot determine causality. All statistics can do is determine if something is going on, or if something is a random occurrence. Only a research design can be used to determine if one variable causes the other variable to change. Never say that a variable causes another variable, or that a result is due to something. People freak out when you tell them that your statistical test indicated that something caused something else. People seem to insist that saying this is akin to committing a sin. While this is an overreaction, saying there is a causal relationship based solely on statistical analysis is inaccurate. But there’s no need to freak out over it. Just say, “something is associated with something else,” and everything will be fine.

    • 10.1: The Meaning of Inferential
      This page discusses inferential statistics, which entails estimating patterns in data to identify whether they arise from randomness or are influenced by underlying factors.
    • 10.2: Patterns
      This page explores the relationship between two variables: the independent variable (IV) and the dependent variable (DV). It discusses statistical tests that examine these relationships, providing examples like gender's effect on help-seeking attitudes and teacher types on autism ratings. The main focus is on identifying how changes in the IV affect the DV, summarized through correlation of variable changes and group comparisons.
    • 10.3: General Linear Model
      This page outlines statistical models, focusing on the general linear model as a tool for predicting outcomes through variable relationships. It compares models to recipes, where independent variables are ingredients leading to measurable results. The text highlights the need for clear descriptions to grasp these relationships and notes that while the general linear model is essential, psychology may demand more complex approaches.
    • 10.4: Discussion Questions
      This page discusses "inferential" methods in statistics for drawing conclusions about populations from sample data. It distinguishes between correlation and causation as main statistical patterns. The general linear model exemplifies the relationship between dependent and independent variables. Additionally, it mentions other statistical models like logistic regression and nonlinear models, which accommodate various data types and relationships beyond linearity.


    This page titled 10: Introduction to Inferential Statistics is shared under a CC BY 4.0 license and was authored, remixed, and/or curated by Peter Ji.