In order to test the hypotheses, we select a random sample of American males in 2006 and measure their weights. The null hypothesis is that there is no change in weight, and therefore the mean weight is still 191 pounds in 2006. The research hypothesis is that the mean weight in men in 2006 is more than 191 pounds. Suppose that an investigator hypothesizes that weights are even higher in 2006 (i.e., that the trend continued over the subsequent 4 years). In 2002, the mean weight for men was reported at 191 pounds. 1 The general trend was that Americans were much heavier and slightly taller in 2002 as compared to 1960 both men and women gained approximately 24 pounds, on average, between 19. The Centers for Disease Control (CDC) reported on trends in weight, height and body mass index from the 1960's through 2002. Here we will focus on procedures for one and two samples when the outcome is either continuous (and we focus on means) or dichotomous (and we focus on proportions). 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).
Facebook critical ops alpha series#
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