Module Components

• Overview

Topics

• Significance Testing and Hypothesis Testing
• Type I and Type II Errors
• Effect Size
• Sample Size

NRSG 795: BIOSTATISTICS FOR EVIDENCE-BASED PRACTICE

Module 4: Inferential Statistics

Overview

Inferential statistics are used to try to reach conclusions that extend beyond the immediate data alone – to the population. This requires the use of assumptions about probability and distributions. For example, a t-test assesses the probability that an observed difference in means between two groups in your sample is one that might have happened by chance. We began using inferential statistics last week and you will see the majority of the course will continue to focus on inferential statistics.

There are two major divisions of inferential statistics:

• “Estimation statistics” is a fancy way of saying that you are estimating population values based on your sample data. A confidence interval gives a range of values for an unknown parameter of the population by measuring a statistical sample. This is expressed in terms of an interval and the degree of confidence that the parameter is within the interval.
• Tests of significance or hypothesis testing tests a claim about the population by analyzing a statistical sample. By design there is some uncertainty in this process. This can be expressed in terms of a level of significance.

This module focuses on hypothesis testing and the risks of error. Research hypotheses cannot be tested directly. Instead it is the absence of a relationship (null hypothesis) that is tested statistically. Based on the rules of negative inference we begin with the assumption that the null hypothesis is true. We then establish a fixed probability as a criterion to objectively mark the cutoff of where we will accept or reject the null relationship (alpha). The probability of correctly rejecting a false null hypothesis is power (1-beta). Related topics include p values, null and alternative hypotheses, and Type 1 and Type 2 errors.

Objectives

At the conclusion of this module, the learner will be able to:

• Apply steps of hypothesis testing to statistical analyses
• Identify sources of Type 1 and Type 2 errors
• Understand the advantages of effect size as compared to p-values.
• Interpret common effect size measures in terms of the magnitude of the effect.
• Determine the effect size from a published study that uses t-tests, odds ratio/relative risk, or Pearson correlation to measure a relationship.

Directions

There are 4 subtopics, videos assigned within the subtopics.