What are experimental hypothesis and null hypotheses
The (or hypotheses -- there may be more than one) is our working hypothesis -- our prediction, or what we expect to happen. It is also called the - because it is an alternative to the null hypothesis. Technically, the claim of the research hypothesis is that with respect to the outcome variable, our samples are from different populations (remember that refers to the group from which the sample is drawn). If we predict that math tutoring results in better performance, than we are predicting that after the treatment (tutoring), the treated sample truly is different from the untreated one (and therefore, from a different population).
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An independent samples design with 20 students chosen by opportunity sampling was used to test the experimental hypothesis: The null hypothesis stated:
Generally, when comparing or contrasting groups (samples), the null hypothesis is that the difference between means (averages) = 0. For categorical data shown on a contingency table, the null hypothesis is that any differences between the observed frequencies (counts in categories) and expected frequencies are due to chance.
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the opposite of the research hypothesis. The null hypothesis states that any effects observed after treatment (or associated with a predictor variable) are due to chance alone. Statistically, the question that is being answered is "If these samples came from the same population with regard to the outcome, how likely is the obtained result?"
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The inferential statistics do not directly address the testable statement (research hypothesis). They address the . Statistically, we test "not." Here are the null hypotheses:
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On the other hand, the null hypothesis is straightforward -- what is the probability that our treated and untreated samples are from the same population (that the treatment or predictor has no effect)? There is only one set of statistical probabilities -- calculation of chance effects. Instead of directly testing H, we test H. If we can reject H, (and factors are under control), we can accept H. To put it another way, the fate of the research hypothesis depends upon what happens to H.
Alternate Hypothesis: - The prediction you are making
When we pose a research question, we want to know whether the outcome is due to the treatment (independent variable) or due to chance (in which case our treatment is probably not effective). For example, the claim that tutoring improves math performance generally does not predict exactly how much improvement. Each level of improvement has a different probability associated with it, and it would take a long time and a great deal of effort to specify the probability of each of the possible outcomes that would support our research hypothesis.
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Using the .05 level of significance means if the null hypothesis is true, we would get our result 5 times out of 100 (or 1 out of 20). We take the risk that our study is not one of those 5 out of 100. Rejecting or accepting the null hypothesis is a gamble. There is always a possibility that we are making a mistake in rejecting the null hypothesis. This is called a - rejecting the null hypothesis when it is true. If we use a .01 cutoff, the chance of a Type I Error is 1 out of 100. With a .05 level of significance, we are taking a bigger gamble. There is a 1/20 (5 out of 100) chance that we are wrong, and that our treatment (or predictor variable) doesn't really matter.