Which is an invalid alternative hypothesis

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Lorem ipsum dolor sit amet, consectetur adipisicing elit. Odit molestiae mollitia laudantium assumenda nam eaque, excepturi, soluta, perspiciatis cupiditate sapiente, adipisci quaerat odio voluptates consectetur nulla eveniet iure vitae quibusdam? Excepturi aliquam in iure, repellat, fugiat illum voluptate repellendus blanditiis veritatis ducimus ad ipsa quisquam, commodi vel necessitatibus, harum quos a dignissimos. Close Save changes. Help F1 or? Basic Terms Section The first step in hypothesis testing is to set up two competing hypotheses.

The two hypotheses are named the null hypothesis and the alternative hypothesis. The null hypothesis states the "status quo". This hypothesis is assumed to be true until there is evidence to suggest otherwise. This is the statement that one wants to conclude. It is also called the research hypothesis. Example Section A man, Mr.

Putting this in a hypothesis testing framework, the hypotheses being tested are: The man is guilty The man is innocent Let's set up the null and alternative hypotheses. Orangejuice is guilty Remember that we assume the null hypothesis is true and try to see if we have evidence against the null.

Test if the percentage of U. In a hypothesis test , sample data is evaluated in order to arrive at a decision about some type of claim. If certain conditions about the sample are satisfied, then the claim can be evaluated for a population.

In a hypothesis test, we: Evaluate the null hypothesis , typically denoted with H 0. The null is not rejected unless the hypothesis test shows otherwise. If we reject the null hypothesis, then we can assume there is enough evidence to support the alternative hypothesis.

Never state that a claim is proven true or false. Keep in mind the underlying fact that hypothesis testing is based on probability laws; therefore, we can talk only in terms of non-absolute certainties.

H 0 and H a are contradictory. The parameter being tested. Is a population proportion. Click 'Join' if it's correct. Emily J. View Full Video Already have an account? Tyler M. Answer A null and alternative hypothesis is given.

Section 1 The Language of Hypothesis Testing. Discussion You must be signed in to discuss. Video Transcript all right. Upgrade today to get a personal Numerade Expert Educator answer! Ask unlimited questions. Test yourself. Join Study Groups. Create your own study plan. The steps are as follows:. Following this logic, we can begin to understand why Mehl and his colleagues concluded that there is no difference in talkativeness between women and men in the population.

Therefore, they retained the null hypothesis—concluding that there is no evidence of a sex difference in the population. We can also see why Kanner and his colleagues concluded that there is a correlation between hassles and symptoms in the population.

Therefore, they rejected the null hypothesis in favour of the alternative hypothesis—concluding that there is a positive correlation between these variables in the population. A crucial step in null hypothesis testing is finding the likelihood of the sample result if the null hypothesis were true. This probability is called the p value. A low p value means that the sample result would be unlikely if the null hypothesis were true and leads to the rejection of the null hypothesis.

A high p value means that the sample result would be likely if the null hypothesis were true and leads to the retention of the null hypothesis. But how low must the p value be before the sample result is considered unlikely enough to reject the null hypothesis? When this happens, the result is said to be statistically significant. This does not necessarily mean that the researcher accepts the null hypothesis as true—only that there is not currently enough evidence to conclude that it is true.

The p value is one of the most misunderstood quantities in psychological research Cohen, [1]. Even professional researchers misinterpret it, and it is not unusual for such misinterpretations to appear in statistics textbooks! The most common misinterpretation is that the p value is the probability that the null hypothesis is true—that the sample result occurred by chance.

For example, a misguided researcher might say that because the p value is. But this is incorrect. The p value is really the probability of a result at least as extreme as the sample result if the null hypothesis were true. So a p value of.

You can avoid this misunderstanding by remembering that the p value is not the probability that any particular hypothesis is true or false. Instead, it is the probability of obtaining the sample result if the null hypothesis were true. Specifically, the stronger the sample relationship and the larger the sample, the less likely the result would be if the null hypothesis were true.

That is, the lower the p value. This should make sense. If there were really no sex difference in the population, then a result this strong based on such a large sample should seem highly unlikely. If there were no sex difference in the population, then a relationship this weak based on such a small sample should seem likely.

And this is precisely why the null hypothesis would be rejected in the first example and retained in the second.