In estimation we focused explicitly on techniques for one and two samples and discussed estimation for a specific parameter (e.g., the mean or proportion of a population), for differences (e.g., difference in means, the risk difference) and ratios (e.g., the relative risk and odds ratio). whether the comparison groups are independent (i.e., physically separate such as men versus women) or dependent (i.e., matched or paired such as pre- and post-assessments on the same participants).the number of comparison groups in the investigation.

the type of outcome variable being analyzed (continuous, dichotomous, discrete).

The techniques for hypothesis testing depend on

Differentiate hypothesis testing procedures based on type of outcome variable and number of sample.

Explain the relationship between confidence interval estimates and p-values in drawing inferences.Explain the difference between one and two sided tests of hypothesis.Distinguish between Type I and Type II errors and discuss the implications of each.Define null and research hypothesis, test statistic, level of significance and decision rule.Learning ObjectivesĪfter completing this module, the student will be able to: The next two modules in this series will address analysis of variance and chi-squared tests. This module will focus on hypothesis testing for means and proportions. Similar to estimation, the process of hypothesis testing is based on probability theory and the Central Limit Theorem. One selects a random sample (or multiple samples when there are more comparison groups), computes summary statistics and then assesses the likelihood that the sample data support the research or alternative hypothesis. The process of hypothesis testing involves setting up two competing hypotheses, the null hypothesis and the alternate hypothesis.

The hypothesis is based on available information and the investigator's belief about the population parameters. This is the first of three modules that will addresses the second area of statistical inference, which is hypothesis testing, in which a specific statement or hypothesis is generated about a population parameter, and sample statistics are used to assess the likelihood that the hypothesis is true. Hypothesis Testing for Means & Proportionsīoston University School of Public Health

Hypothesis test calculator