Difference Between Hypothesis and Research Question
The other kind of error that is possible occurs when we do not reject a null hypothesis that is false.
the null hypothesis is rejected when it is true b.
where the observed sample mean, μ_{0} = value specified in null hypothesis, s = standard deviation of the sample measurements and n = the number of differences.
One of the main goals of statistical hypothesis testing is to estimate the P value, which is the probability of obtaining the observed results, or something more extreme, if the null hypothesis were true. If the observed results are unlikely under the null hypothesis, your reject the null hypothesis. Alternatives to this "frequentist" approach to statistics include Bayesian statistics and estimation of effect sizes and confidence intervals.
the null hypothesis is not rejected when it is false c.
A well written hypothesis contains two elements, the Null hypothesis and the Alternate hypothesis. Writing a clear hypothesis that can be quickly analyzed with a statistical test is a skill that will be illustrated and practiced in this lesson.
False. The researcher is claimingthat (1  pvalue) is the probability that the alternative hypothesisis false. The pvalue is not a probability of an alternative (or null) hypothesisbeing true or false. See the answer to part c.
failing to reject the null hypothesis when it is false.
where the observed sample mean difference, μ_{0} = value specified in null hypothesis, s_{d} = standard deviation of the differences in the sample measurements and n = sample size. For instance, if we wanted to test for a difference in mean SAT Math and mean SAT Verbal scores, we would random sample subjects, record their SATM and SATV scores in two separate columns, then create a third column that contained the differences between these scores. Then the sample mean and sample standard deviation would be those that were calculated on this column of differences.
Notice that the top part of the statistic is the difference between the sample mean and the null hypothesis. The bottom part of the calculation is the standard error of the mean.
failing to reject the null hypothesis when it is true.

rejecting the null hypothesis when it is true.
Your evidence may allow you to reject your null hypotheses, thus lending support to your experimental hypothesis.

rejecting the null hypothesis when it is false.
the probability of rejecting the null hypothesis when the null hypothesis is true.

rejecting the null hypothesis when the alternative is true.
If we would reject a null hypothesis at the 5% level, we would also reject it at the 1% level.
not rejecting the null hypothesis when the alternative is true.
This number, 0.030, is the P value. It is defined as the probability of getting the observed result, or a more extreme result, if the null hypothesis is true. So "P=0.030" is a shorthand way of saying "The probability of getting 17 or fewer male chickens out of 48 total chickens, IF the null hypothesis is true that 50% of chickens are male, is 0.030."
the null hypothesis is rejected when it is true.
After you do a statistical test, you are either going to reject or accept the null hypothesis. Rejecting the null hypothesis means that you conclude that the null hypothesis is not true; in our chicken sex example, you would conclude that the true proportion of male chicks, if you gave chocolate to an infinite number of chicken mothers, would be less than 50%.
The failure to reject does not imply the null hypothesis is true.
Descriptive research such as , and surveys are often used when it would be impossible or difficult to . These methods are best used to describe different aspects of a behavior or psychological phenomenon. Once a researcher has collected data using descriptive methods, a can then be used to look at how the variables are related. This type of research method might be used to investigate a hypothesis that is difficult to text experimentally.
RESEARCH QUESTIONS AND HYPOTHESES
Once a researcher has formed a testable hypothesis, the next step is to select a research design and start collecting data. The research method a researcher chooses depends largely on exactly what they are studying.
The null hypothesis and alternative hypothesis are required to be ..
When you reject a null hypothesis, there's a chance that you're making a mistake. The null hypothesis might really be true, and it may be that your experimental results deviate from the null hypothesis purely as a result of chance. In a sample of 48 chickens, it's possible to get 17 male chickens purely by chance; it's even possible (although extremely unlikely) to get 0 male and 48 female chickens purely by chance, even though the true proportion is 50% males. This is why we never say we "prove" something in science; there's always a chance, however miniscule, that our data are fooling us and deviate from the null hypothesis purely due to chance. When your data fool you into rejecting the null hypothesis even though it's true, it's called a "false positive," or a "Type I error." So another way of defining the P value is the probability of getting a false positive like the one you've observed, if the null hypothesis is true.