# My understanding of null and alternate hypotheses :

**Born in Omaha,** Nebraska, Halperin graduated from the University of Omaha in 1940 with a degree in mathematics. In 1950 he obtained a Ph.D. degree from the University of North Carolina. His career began as a research mathematician at the RAND corporation and posts at the National Institutes of Health and the Division of Biologic Standards followed. Later he became Research Professor of Statistics and Director of the Biostatistics Center of the Department of Statistics at the George Washington University. Halperin made important contributions in many areas of statistics including multivariate analysis, regression analysis, multiple comparisons and the detection of outliers. He died on 1 February 1988 in Fairfax, Virginia.

## The techniques for hypothesis testing depend on

### Next section: to Inferential statistics (testing hypotheses)

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. The hypothesis is based on available information and the investigator's belief about the population parameters. The process of hypothesis testing involves setting up two competing hypotheses, the null hypothesis and the alternate hypothesis. 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. Similar to estimation, the process of hypothesis testing is based on probability theory and the Central Limit Theorem.

### Â c)Â Is the null hypothesis rejected at:

This module will focus on hypothesis testing for means and proportions. The next two modules in this series will address analysis of variance and chi-squared tests.

## H0: To Hypothesis testing (Statistics) - what-when-how

Smoking has been shown over and over to be a risk factor for cardiovascular disease. What might explain the fact that we did not observe a statistically significant difference using data from the Framingham Heart Study? HINT: Here we consider prevalent CVD, would the results have been different if we considered incident CVD?

## 06/01/2018 · H0 Symbol for null hypothesis

A 95% confidence interval for the difference in prevalent CVD (or risk difference) between smokers and non-smokers as 0.0114 __+__ 0.0247, or between -0.0133 and 0.0361. Because the 95% confidence interval for the risk difference includes zero we again conclude that there is no statistically significant difference in prevalent CVD between smokers and non-smokers.