The null hypothesis is considered the default in a scientific experiment. In contrast, an alternative hypothesis is one that claims that there is a meaningful relationship between two phenomena. These two competing hypotheses can be compared by performing a statistical hypothesis test, which determines whether there is a statistically significant relationship between the data.
For example, scientists studying the water quality of a stream may wish to determine whether a certain chemical affects the acidity of the water.
The null hypothesis—that the chemical has no effect on the water quality—can be tested by measuring the pH level of two water samples, one of which contains some of the chemical and one of which has been left untouched.
If the sample with the added chemical is measurably more or less acidic—as determined through statistical analysis—it is a reason to reject the null hypothesis. If the sample's acidity is unchanged, it is a reason to not reject the null hypothesis.
When scientists design experiments, they attempt to find evidence for the alternative hypothesis. They do not try to prove that the null hypothesis is true. The null hypothesis is assumed to be an accurate statement until contrary evidence proves otherwise. As a result, a test of significance does not produce any evidence pertaining to the truth of the null hypothesis. In an experiment, the null hypothesis and the alternative hypothesis should be carefully formulated such that one and only one of these statements is true.
If the collected data supports the alternative hypothesis, then the null hypothesis can be rejected as false. However, if the data does not support the alternative hypothesis, this does not mean that the null hypothesis is true.
All it means is that the null hypothesis has not been disproven—hence the term "failure to reject. Using this convention, tests of significance allow scientists to either reject or not reject the null hypothesis. Now that we have reviewed the critical value and P -value approach procedures for each of three possible hypotheses, let's look at three new examples — one of a right-tailed test, one of a left-tailed test, and one of a two-tailed test.
The good news is that, whenever possible, we will take advantage of the test statistics and P -values reported in statistical software, such as Minitab, to conduct our hypothesis tests in this course.
Breadcrumb Home reviews statistical concepts hypothesis testing p value approach. Specifically, the four steps involved in using the P -value approach to conducting any hypothesis test are: Specify the null and alternative hypotheses.
The first step in hypothesis testing is to set up two competing hypotheses. The hypotheses are the most important aspect. If the hypotheses are incorrect, your conclusion will also be incorrect.
The goal of hypothesis testing is to see if there is enough evidence against the null hypothesis. In other words, to see if there is enough evidence to reject the null hypothesis. If there is not enough evidence, then we fail to reject the null hypothesis. A man, Mr. Orangejuice, goes to trial and is tried for the murder of his ex-wife. He is either guilty or innocent.
Set up the null and alternative hypotheses for this example. Remember that we assume the null hypothesis is true and try to see if we have evidence against the null.
Therefore, it makes sense in this example to assume the man is innocent and test to see if there is evidence that he is guilty. We want to know the answer to a research question. We determine our null and alternative hypotheses. Now it is time to make a decision. Consider the following table. That is, a protein might be "dormant" and the stimulus you are using can only possibly "wake it up" i. In addition, for some statistical tests, one-tailed tests are not possible.
If our statistical analysis shows that the significance level is below the cut-off value we have set e. Alternatively, if the significance level is above the cut-off value, we fail to reject the null hypothesis and cannot accept the alternative hypothesis.
You should note that you cannot accept the null hypothesis, but only find evidence against it. Hypothesis Testing cont Hypothesis Testing The null and alternative hypothesis In order to undertake hypothesis testing you need to express your research hypothesis as a null and alternative hypothesis. Alternative Hypothesis H A : Undertaking seminar class has a positive effect on students' performance. As such, we can state: Null Hypotheses H 0 : The mean exam mark for the "seminar" and "lecture-only" teaching methods is the same in the population.
Alternative Hypothesis H A : The mean exam mark for the "seminar" and "lecture-only" teaching methods is not the same in the population. Hypothesis Testing Significance levels The level of statistical significance is often expressed as the so-called p -value.
Hypothesis Testing One- and two-tailed predictions When considering whether we reject the null hypothesis and accept the alternative hypothesis, we need to consider the direction of the alternative hypothesis statement. For example, the alternative hypothesis that was stated earlier is: Alternative Hypothesis H A : Undertaking seminar classes has a positive effect on students' performance. If Sarah had made a two-tailed prediction, the alternative hypothesis might have been: Alternative Hypothesis H a : Undertaking seminar classes has an effect on students' performance.
Hypothesis Testing Rejecting or failing to reject the null hypothesis Let's return finally to the question of whether we reject or fail to reject the null hypothesis. Null Hypotheses H 0 :.
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