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states that there is no difference ..

By rejecting equality, that is, the null hypothesis, you assert that there is a difference.

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Null hypothesis: There will be no difference in public ..

A brief note… Remember—

Descriptive statistics
Describing/condensing data to make it understandable (frequencies, mean, variance, SD)
Inferential statistics
Test null hypothesis; estimate the likelihood your results are true to population (that the null is true/false)
T-test, ANOVA, chi-square, regression, etc.

The F-statistic is not large enough; therefore, one must reject the null hypothesis.

In order to undertake hypothesis testing you need to express your research hypothesis as a null and alternative hypothesis. The null hypothesis and alternative hypothesis are statements regarding the differences or effects that occur in the population. You will use your sample to test which statement (i.e., the null hypothesis or alternative hypothesis) is most likely (although technically, you test the evidence against the null hypothesis). So, with respect to our teaching example, the null and alternative hypothesis will reflect statements about all statistics students on graduate management courses.

The null hypothesis states that there is no significant ..

The null hypothesis is essentially the "devil's advocate" position. That is, it assumes that whatever you are trying to prove did not happen (hint: it usually states that something equals zero). For example, the two different teaching methods did not result in different exam performances (i.e., zero difference). Another example might be that there is no relationship between anxiety and athletic performance (i.e., the slope is zero). The alternative hypothesis states the opposite and is usually the hypothesis you are trying to prove (e.g., the two different teaching methods did result in different exam performances). Initially, you can state these hypotheses in more general terms (e.g., using terms like "effect", "relationship", etc.), as shown below for the teaching methods example:

A small p-value favors the alternative hypothesis. A small p-value means the observed data would not be very likely to occur if we believe the null hypothesis is true. So we believe in our data and disbelieve the null hypothesis. An easy (hopefully!) way to grasp this is to consider the situation where a professor states that you are just a 70% student. You doubt this statement and want to show that you are better that a 70% student. If you took a random sample of 10 of your previous exams and calculated the mean percentage of these 10 tests, which mean would be less likely to occur if in fact you were a 70% student (the null hypothesis): a sample mean of 72% or one of 90%? Obviously the 90% would be less likely and therefore would have a small probability (i.e. p-value).

The null hypothesis states that there is no ..

Now that you have identified the null and alternative hypotheses, you need to find evidence and develop a strategy for declaring your "support" for either the null or alternative hypothesis. We can do this using some statistical theory and some arbitrary cut-off points. Both these issues are dealt with next.

The level of statistical significance is often expressed as the so-called p-value. Depending on the statistical test you have chosen, you will calculate a probability (i.e., the p-value) of observing your sample results (or more extreme) given that the null hypothesis is true. Another way of phrasing this is to consider the probability that a difference in a mean score (or other statistic) could have arisen based on the assumption that there really is no difference. Let us consider this statement with respect to our example where we are interested in the difference in mean exam performance between two different teaching methods. If there really is no difference between the two teaching methods in the population (i.e., given that the null hypothesis is true), how likely would it be to see a difference in the mean exam performance between the two teaching methods as large as (or larger than) that which has been observed in your sample?

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  • Difference Between Null and Alternative

    06/03/2017 · The null hypothesis states there is no relationship ..

  • 16/01/2018 · Hypothesis Test: Difference Between Means

    The null hypothesis states there is no difference in the expected and observed ratios of flies in this cross.

  • STATISTICS Flashcards | Quizlet

    The null hypothesis (H0) states that there is no difference between the groups being tested

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There are also at least four goals of null hypotheses for ..

Null Hypothesis Research Hypothesis: Researcher’s expectation, prediction; also called hypothesis or alternative hypothesis

Null Hypothesis: No differences, no effects; differences or effects observed are the result of sampling error What you’re doing with statistics… Have a question/topic
Consult literature; create hypothesis (has an accompanying null hypothesis)
Sample a population and measure
Use statistics to estimate probability of your results being true to population; ruling out your null hypothesis to test the (research) hypothesis Significance Testing Test If you set your significance level at .05 and you get the following, significant or not?

The problems with p-values are not just with p-values: …

So, you might get a p-value such as 0.03 (i.e., p = .03). This means that there is a 3% chance of finding a difference as large as (or larger than) the one in your study given that the null hypothesis is true. However, you want to know whether this is "statistically significant". Typically, if there was a 5% or less chance (5 times in 100 or less) that the difference in the mean exam performance between the two teaching methods (or whatever statistic you are using) is as different as observed given the null hypothesis is true, you would reject the null hypothesis and accept the alternative hypothesis. Alternately, if the chance was greater than 5% (5 times in 100 or more), you would fail to reject the null hypothesis and would not accept the alternative hypothesis. As such, in this example where p = .03, we would reject the null hypothesis and accept the alternative hypothesis. We reject it because at a significance level of 0.03 (i.e., less than a 5% chance), the result we obtained could happen too frequently for us to be confident that it was the two teaching methods that had an effect on exam performance.

ANOVA Test: Definition, Types, Examples - Statistics …

Null Hypothesis Hypothesis: Females will have higher public speaking scores than males.

Null hypothesis: There will be no difference in public speaking scores OR Females and males will have equal public speaking scores.

Roger Clemens, Barry Bonds , Performance-Enhancing …

What you’re doing… Null hypothesis: remember—states that observed difference created by sampling error (no real differences)
Testing if the null hypothesis is true or not: significance testing
You use inferential statistics.

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