Sign Test Flashcards
What is the feature of a sign test?
The sign test ignores the magnitude of the different scores and considers only their direction or sign. This omits a lot of information and makes the test rather insensitive, but much easier to understand.
The crucial question is:” How unlikely is it?”
What is a repeated measures design?
The same subjects are used in each condition. There are paired scores in the conditions, and the differences between the paired scores are analyzed.
What is an alternative hypothesis (H1)?
The alternative hypothesis is the one that claims the difference in results between conditions is due to the independent variable.
Can be directional or nondirectional.
What’s the relationship between the null hypothesis (H0) and the alternative hypothesis (H1)?
Null hypothesis is set up to be the logical counterpart of the alternative hypothesis. If H0 is false, H1 must be true. The two hypotheses are mutually exclusive and exhaustive.
How do we evaluate the null hypothesis?
We evaluate the null hypothesis by assuming it is true and testing the reasonableness of this assumption by calculating the probability of getting the results if chance alone is operating.
If the obtained probability turns out to be equal to or less than a critical probability level called the alpha (a) level, we reject the null hypothesis. Rejecting the null hypothesis allows us, then, to indirectly accept the alternative hypothesis.
What is the decision rule regarding alpha level and H0?
If the obtained probability 小于等于 alpha, reject H0.
If the obtained probability 大于alpha, fails to reject H0, retain H0.
What is the definition of type I and type II error?
A Type I error is defined as rejecting the null hypothesis when it is in fact true.
A Type II error is retaining a null hypothesis when it is actually false.
Controlling the alpha level can help us minimize the probability of making a Type I error.
“Why not set even more stringent criteria, such as a = 0.001?”
When the alpha level is made more stringent, the probability of making a Type II error increases.
What are some alpha levels to be set under different conditions?
Depends on the intended use of the experimental results.
1. If the results are to communicate a new fact to the scientific
community, the consequences of a Type I error are great, and therefore stringent alpha
levels are used (0.05 and 0.01).
If, however, the experiment is
exploratory in nature, and the results are to guide the researcher in deciding whether to do a full-fledged experiment. It would be foolish to use such stringent levels. In such cases, alpha levels as high as 0.10 or 0.20 are often used.
Why would it be incorrect to use just the specific outcome when evaluating the results of an experiment?
We must determine the probability of getting the obtained outcome or any outcome that is even more extreme (depending on the tails). It is this probability that we compare with alpha to assess the reasonableness of the null hypothesis.
Nondirectional = both tails
Directional = one tail
What is the rule used to determine whether the probability evaluation should be one- or two-tailed?
The evaluation should always be two-tailed, unless the experimenter will retain H0 when results are extreme in the direction opposite to the predicted direction.
What is the difference between significant and important?
Being statistically significant doesn’t necessarily mean the result is important. Generally, the importance of an effect depends on the size of the effect.
Under what conditions is it legitimate to use a directional
H1? Why is it not legitimate to use a directional
H1 just because the experimenter has a “hunch” about
the direction?
Unless the scientist will conclude by retaining H0 if the results turn out
to be extreme in the opposite direction, he or she should use a nondirectional H1 and a two-tailed evaluation, even though his or her hunch is directional.
