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Basic Statistics in Multivariate Analysis$
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Karen A. Randolph and Laura L. Myers

Print publication date: 2013

Print ISBN-13: 9780199764044

Published to Oxford Scholarship Online: May 2013

DOI: 10.1093/acprof:oso/9780199764044.001.0001

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Inferential Statistics

Inferential Statistics

Chapter:
(p.35) 3 Inferential Statistics
Source:
Basic Statistics in Multivariate Analysis
Author(s):

Karen A. Randolph

Laura L. Myers

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780199764044.003.0003

Chapter 3 focuses on inferential statistics, statistical methods that use data observed in a sample to make hypotheses or predictions about data that have not been observed in the larger population. Inferential statistics make it possible to use descriptive statistical methods based on a sample to estimate population parameters, as well as predict whether relationships between variables found in a sample will hold true for the larger population. This chapter describes different types of hypotheses, including the null hypothesis, one-tailed or directional research hypothesis, and two-tailed or non-directional research hypothesis. Relationships between variables (i.e., association, correlation, and causation) as well as different types of variables (i.e., independent, dependent, predictor, outcome, moderating, confounding, obscuring or suppressor, intervening or mediating, and control variables) are also described. Next, the chapter offers detailed instructions on how to calculate and use percentiles and z scores. It explains how the sampling distribution of means can be used to predict information about the larger population, as well as how the sampling distribution of the difference between means can be used to determine if two samples do or do not come from the same population. Other statistical concepts, such as p-value, rejection levels, Type I and Type II errors, statistical power, and effect size, are presented, as well as a discussion of the meaning of statistically significant research findings.

Keywords:   effect size, hypothesis testing, inferential statistics, normal distribution, null hypothesis, p-value, research hypothesis, type i error, type ii error

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