Yes; the null hypothesis, H0, always includes an equality. The alternative hypothesis, H1, is >, <, or does not equal.
H0: μ = 6
symbol for research hypothesis are written in two ways . Ha or H1 . both meant to address research hypothesis.
== == A random sample of 15 observations from the first population revealed a sample mean of 350 and a sample standard deviation of 12. A random sample of 17 observations from the second population revealed a sample mean of 342 and a sample standard deviation of 15. At the .10 significance level, is there a difference in the population means? ***Please show all the steps and work you used to conclude an answer and explain it as you would to a simpleton. Thank you so much. Read more: http://www.justanswer.com/questions/h0ee-null-hypothesis-h0-u1-u2alternate#ixzz0N7q0A7Ns
A confidence interval, for a given probability, is the interval within which the true value may be found with that probability if the null hypothesis is true. There are two possible reasons why a confidence interval may be asymmetrical. One is that the alternative hypothesis is asymmetrical: for example, H0 is X = 5 and H1 is X > 5 (rather than X ≠5). The other possible reason is that the test statistic has an asymmetrical distribution. Either of these can give rise to asymmetrical CIs.
It tells us that H1,H0 (alternative )hypothesis is selected
Yes; the null hypothesis, H0, always includes an equality. The alternative hypothesis, H1, is >, <, or does not equal.
There are two types of errors associated with hypothesis testing. Type I error occurs when the null hypothesis is rejected when it is true. Type II error occurs when the null hypothesis is not rejected when it is false. H0 is referred to as the null hypothesis and Ha (or H1) is referred to as the alternative hypothesis.
okay wikianswers messed up the question. H0: M=23; H1: M not 23; n=50; x-bar = 21.25; standard deviation = 5. please help!
H0: μ = 6
The alternativehypothesis (Ha or H1) describes the population parameters that the sample data represent, if the predicted relationship exists. It is always the hypothesis of difference. That is as opposed to the null hypothesis (H0) that describes the population parameters that the sample data represent if the predicted relationship does not exist. See Basic Statistics of the Behavioral Sciences by Heiman.
A statistical hypothesis test will usually be performed by inductively comparing results of experiments or observations. The number or amount of comparisons will generally dictate the statistical test to use. The researcher is basically making a statement and assuming that it is either correct (the hypothesis - H1) or assuming that it is incorrect (the null hypothesis - H0) and testing that assumption within a predetermined significance level - the alpha.
- Status quo- what is commonly believed or accepted- Status quo- is always represented by Ho (null hypothesis)Ex. Should we introduce a new product/service? Status quo: not introduce itEx. Should we stop engaging in a marketing action? Status quo: keep goingNull hypothesisInterpretation of statistical information can often involve the development of a null-hypothesis in that the assumption is that whatever is proposed as a cause has no effect on the variable being measured.The best illustration for a novice is the predicament encountered by a jury trial. The null hypothesis, H0, asserts that the defendant is innocent, whereas the alternative hypothesis, H1, asserts that the defendant is guilty. The indictment comes because of suspicion of the guilt. The H0 (status quo) stands in opposition to H1 and is maintained unless H1 is supported by evidence "beyond a reasonable doubt". However,"failure to reject H0" in this case does not imply innocence, but merely that the evidence was insufficient to convict. So the jury does not necessarily accept H0 but fails to reject H0. While one can not "prove" a null hypothesis one can test how close it is to being true with a, which tests for type II errorsRead more: statistics
When the alternative hypothesis is non-directional, we use a two-tailed test. Example: H0: mean = 50 Ha : mean not equal to 50 Here is a directional hypothesis that would use a one-tailed test. H0: mean = 40 Ha : mean > 40 or H0: mean = 40 Ha: mean < 40
Either H1 or Ha.
H1 hypothesis is rejected when the p-value associated with the test statistic is less than the significance level (usually 0.05) chosen for the hypothesis test. This indicates that the data provides enough evidence to reject the alternative hypothesis in favor of the null hypothesis.
symbol for research hypothesis are written in two ways . Ha or H1 . both meant to address research hypothesis.