Null and Alternative Hypothesis  Real Statistics Using …
Need to state both null and research hypothesis instatistical terms too.
Hypothesis Testing Flashcards  Quizlet
Another way your data can fool you is when you don't reject the null hypothesis, even though it's not true. If the true proportion of female chicks is 51%, the null hypothesis of a 50% proportion is not true, but you're unlikely to get a significant difference from the null hypothesis unless you have a huge sample size. Failing to reject the null hypothesis, even though it's not true, is a "false negative" or "Type II error." This is why we never say that our data shows the null hypothesis to be true; all we can say is that we haven't rejected the null hypothesis.
It is important to distinguish between biological null and alternative hypotheses and statistical null and alternative hypotheses. "Sexual selection by females has caused male chickens to evolve bigger feet than females" is a biological alternative hypothesis; it says something about biological processes, in this case sexual selection. "Male chickens have a different average foot size than females" is a statistical alternative hypothesis; it says something about the numbers, but nothing about what caused those numbers to be different. The biological null and alternative hypotheses are the first that you should think of, as they describe something interesting about biology; they are two possible answers to the biological question you are interested in ("What affects foot size in chickens?"). The statistical null and alternative hypotheses are statements about the data that should follow from the biological hypotheses: if sexual selection favors bigger feet in male chickens (a biological hypothesis), then the average foot size in male chickens should be larger than the average in females (a statistical hypothesis). If you reject the statistical null hypothesis, you then have to decide whether that's enough evidence that you can reject your biological null hypothesis. For example, if you don't find a significant difference in foot size between male and female chickens, you could conclude "There is no significant evidence that sexual selection has caused male chickens to have bigger feet." If you do find a statistically significant difference in foot size, that might not be enough for you to conclude that sexual selection caused the bigger feet; it might be that males eat more, or that the bigger feet are a developmental byproduct of the roosters' combs, or that males run around more and the exercise makes their feet bigger. When there are multiple biological interpretations of a statistical result, you need to think of additional experiments to test the different possibilities.
rejecting the null hypothesis when it is true (Type I error) ..
As you make decisions based on alpha = 0.05, that means your decision point of mean of sample is 1.65: if the mean of sample is bigger than 1.65, you reject null; if smaller than 1.65, you accept it (like in our case where mean is 1). So beta equals to the probability that you find mean of sample is
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.
a true null hypothesis, we committed a type I error
Through statistical analysis, which we will get to shortly, our job will be to make a decision based on our analysis to either reject the null hypothesis or to fail to reject the null hypothesis. The decision ALWAYS reflects the null hypothesis, and yes, that is the proper wording.
You null hypothesis is “mean of A is 0″. You measured A 100 times. The mean is 1 and standard deviation is 10 (and thus the standard deviation of mean is 10/sqrt(100)=1). You are deciding if the null hypothesis should be rejected and what is beta if alpha is 0.05. You calculate and find the p value is 0.1587, which is bigger than 0.05 so you accept the null hypothsis.
The Null Hypothesis, the Alternative Hypothesis, and …

Null Hypothesis, pValue, Statistical Significance, Type …
If, for example, the null hypothesis says two population means are equal, the alternative says the means are unequal.

null hypothesis (convict) Type I error ..
Therefore, if the null hypothesis is true , the level of the test, is the probability of a type I error.

What do significance levels and P values mean in hypothesis tests
19/03/2015 · What do significance levels and P values mean in hypothesis tests
What is a Null Hypothesis?  Definition & Examples  …
When you perform a large number of statistical tests, some will have P values less than 0.05 purely by chance, even if all your null hypotheses are really true. The Bonferroni correction is one simple way to take this into account; adjusting the false discovery rate using the BenjaminiHochberg procedure is a more powerful method.
Null Hypothesis  University of North Carolina at Wilmington
Any time you reject a because a P value is less than your critical value, it's possible that you're wrong; the null hypothesis might really be true, and your significant result might be due to chance. A P value of 0.05 means that there's a 5% chance of getting your observed result, if the null hypothesis were true. It does not mean that there's a 5% chance that the null hypothesis is true.
We fail to reject null hypothesis when pvalue ..
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:
a type I error occurs when the null hypothesis is actually ..
For example, if you do 100 statistical tests, and for all of them the null hypothesis is actually true, you'd expect about 5 of the tests to be significant at the P
by failing to reject a true Null Hypothesis, p = (1alpha) ..
So, you might get a pvalue 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.