﻿ alternative and null hypothesis testing

# alternative and null hypothesis testing

The opposite of the null hypothesis is known as the alternative hypothesis. Difference Between Null and Alternative.Refuting the null hypothesis would require showing statistical significance, which can be found using a variety of tests. Hypothesis testing is formulated in terms of two hypotheses: H0: the null hypothesis H1: the alternate hypothesis.However, we can compute it for testing H0 : p 0.05 against the alternative hypothesis that H1 : p 0.1, for instance. While the null hypothesis is the hypothesis, which is to be actually tested, whereas alternative hypothesis gives an alternative to the null hypothesis.Null hypothesis This article excerpt shed light on the fundamental differences between null and alternative hypothesis. Return to Behavioral Research Methods. When testing a hypothesis, you are actually making an indirect comparison between the populations of your samples. Since samples are not perfectly representative of populations, the results of a hypothesis test is a statistically supported guess. Alternative hypothesis testing method was opposed by Ronald Fisher.Even hypothesis testing in modern statistics adopts the same type of test with the extension of alternative hypothesis since it could just be a negation of our null hypothesis. Introduction, the null and alternative hypotheses Hypothesis testing process Type I and Type II errors, power Test statistic, level of signicance and rejection/acceptance regions in upper-, lower- and two-tail tests Test of hypothesis: procedure p-value Whether the test is two-tailed or one-tailed depends on the alternative hypothesis, whether the advocate can make its case that the null hypothesis is substantially false regardless of which way the data is off nominal. Hypothesis testing is not symmetric.

If you fail to reject the null hypothesis, you cannot rule out that you simply did not have the power to reject.If you interchange the null and alternative, you dont get identical results. Some researchers say that a hypothesis test can have one of two outcomes: you accept the null hypothesis or you reject the null hypothesis.Failure to reject implies that the data are not sufficiently persuasive for us to prefer the alternative hypothesis over the null hypothesis. Hypothesis testing allows us to use a sample to decide between two statements made about a Population characteristic.These two statements are called the Null Hypothesis and the Alternative Hypothesis. The P-value approach involves determining "likely" or "unlikely" by determining the probability — assuming the null hypothesis were true — of observing a more extreme test statistic in the direction of the alternative hypothesis than the one observed. Indeed, the idea of hypothesis testing is also found in the US court system.The p-value is a way of quantifying the strength of the evidence against the null hypothesis and in favor of the alternative. Formally the p-value is a conditional probability, as stated below. A hypothesis test is conducted using a test statistic whose distribution is known under the null hypothesis H0 , and is used to consider the likely truth of the null hypothesis as opposed to a stated alternative hypothesis H1. Denition 4.2 p-value. 60. One-tailed hypothesis testing specifies a direction of the statistical test. For example to test whether cloud seeding increases the average annual rainfall in an area which usually has an average annual rainfall of 20 cm, we define the null and alternative hypotheses as follows Today we will understand: Formulating the null and alternative hypothesis. Distinguish between a one-tail and two-tail hypothesis test.

The above information should in fact be sufficient to determine the null and alternative hypothesis with ease. Some practical examples. For example, say that we are examining a hypothesis testing question from our stats homework The alternative hypothesis, Ha: Is the opposite of the null hypothesis Challenges the status quo Never contains just the sign Is generally the hypothesis that is believed to be true by the researcher. One and Two Sided Tests. Introduction to Hypothesis Testing. I. Terms, Concepts. A.