CHAPTER 6

Fail to reject the null hypothesis

Not having enough statistical strength to show a difference or an association. - You can never "accept" or "prove" the null hypothesis, which is the absence of something. You can only "disprove" it (reject it) or "fail to disprove" it (fail to reject it). - If the p-value is greater than the alpha, the chance of making a type one error is greater than the level you are comfortable with, and you should fail to reject the null hypothesis. - If (p > alpha), you would fail to reject the null hypothesis and report that there is no relationship, association, or difference between the variables.

Hypothesis

An observation or idea that can be tested.

Hypothesis testing

The application of a statistical test to determine whether an observation or idea is to be refuted or accepted. - fancy term for figuring out whether you are right - Process: First, you state your null and alternative hypotheses; then you pick the significance level (alpha) that you wish to have in your study. Remember that, typically, the alpha is 0.05, which means that if you find a difference, you are 95% sure it is truly there, not just a chance occurrence.

Statistical significance

The difference observed between two samples is large enough to conclude that it is not simply due to chance. - If you take two or more representative samples from the same population, you would expect to find approximately the same difference again and again. If you have a statistically significant result, you can reject the null hypothesis.

Alternative hypothesis

Usually the relationship or association or difference that the researcher actually believes to be present. - represented as H1

Clinically significant

A result that is statistically significant and clinically useful. - Large enough to indicate a preferential course of treatment or a difference in clinical approach to patient care. - For example, as a nurse manager, you are approached by the largest chocolate sales team in your region. They say that the newest research shows that patients who receive free chocolate from the hospital are discharged earlier. Well, you might be interested in reading the study. The chocolate team conducted a study with 700,000 participants and found that those who were given free chocolate went home on average 2 minutes earlier than those who didn't. Although you know chocolate makes people feel better, you do not see these statistically significant results as being clinically significant because a saving of 2 minutes has very little impact on your unit. Besides, what do the follow-up studies say about tooth decay? In addition, having a very large sample size (700,000 people) in a study might result in statistical significance even though the difference found (the effect size) is actually very small.

Reject the null hypothesis

Having enough statistical strength to show a difference or an association. - You have determined the difference between the two groups is greater than the difference you might expect to result from chance. - You have evidence of a statistically significant relationship between employment status and receiving a flu shot at your clinic, and you have demonstrated support for your alternative hypothesis. - When a pregnant patient has an ultrasound, the technician attempts to determine the sex of the infant by detecting the presence of a penis. The null hypothesis is that there is no penis. The alternative hypothesis is that there is one. If a penis is detected, the ultrasound technician can state that there is one and that the baby is a boy. If a penis is not detected, the technician cannot be sure that there isn't one; it might be present but undetected (Corty, 2007)

Type one error

Incorrectly rejecting the null hypothesis. - For example, you conduct a study examining the association between eating a high-fiber breakfast and 10 a.m. serum glucose levels. The null hypothesis is that a high-fiber breakfast is not associated with the 10 a.m. serum glucose levels. The alternative is that it is. You select an alpha of 0.05, which means you are willing to accept that there is a 5% chance that you will reject the null hypothesis incorrectly and report that eating a high-fiber breakfast is associated with a change in blood sugar levels at 10 a.m. when in actuality it is not.

Null hypothesis

No difference or association between variables that is any greater or less than would be expected by chance. - represented as H0

Alpha (α)

The significance level, usually 0.05. The probability of incorrectly rejecting the null hypothesis or making a type one error. - preestablished limit on the chance the researcher is willing to take that he or she will report a statistically significant difference that does not exist - An alpha of 0.05 can also be interpreted to mean that the researcher is 95% sure that the significant difference that he or she is reporting is correct. - If the p-value were greater than alpha, our decision would be to fail to reject the null and report that there is no relationship, association, or difference between the variables