## An risk ratio of 1 means there is no difference between the groups

### H0: Null hypothesis (no change, no difference);

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.

### The column 1 relative risk is then computed as

It may seem a little bit backwards to make our decision in terms of the null hypothesis since that hypothesis is based on the notion that what we believe to be true isn't really true! Our legal system provides a good analogy for the logic of this approach. If you are accused of a crime, you are presumed to be innocent (the null hypothesis) until the prosecution can build a strong enough case (the hypothesis test) to reject that presumption in favor of guilt (the alternative hypothesis).

shows a typical printed output of a classical analysis. Since takes only two values, the null hypothesis for no difference between the two groups is identical to the null hypothesis that the regression coefficient for is 0. All three tests in the "Testing Global Null Hypothesis: BETA=0" table (see the section ) suggest that the survival curves for the two pretreatment groups might not be the same. In this model, the hazard ratio (or risk ratio) for , defined as the exponentiation of the regression coefficient for , is the ratio of the hazard functions between the two groups. The estimate is 0.551, implying that the hazard function for =1 is smaller than that for =0. In other words, rats in =1 lived longer than those in =0. This conclusion is also revealed in the plot of the survivor functions in .

## I’m stuck on how to value the null or alternative hypotheses

Theanalysis produces Cochran-Mantel-Haenszel statistics, and for tables, it includes estimation of the common oddsratio, common relative risks, and the Breslow-Day test forhomogeneity of the odds ratios.

## What role do human beings play in this hypothesis.

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). 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).

## We reject the null hypothesis because -6.15

All the two-way test statistics described in this sectiontest the null hypothesis of no association between therow variable and the column variable.

## the null hypothesis is rejected when it is true b.

The key issues related to a decision-maker's preferences regarding alternatives, criteria for choice, and choice modes, together with the risk assessment tools are also presented.

## the null hypothesis is not rejected when it is false c.

If, based on our results, we make the decision to reject the null hypothesis (indicating that there is a significant difference) but those findings where in fact just due to chance and there really was NOT a significant difference, then we just committed a .

## the null hypothesis is probably wrong b.

The reverse of the above situation is also possible. If, again based on our results, we make the decision to fail to reject the null hypothesis (indicating that there is NOT a significant difference) but those findings were again just chance events and there really WAS a significant difference then we just committed a .