Assignment 5 - Hypothesis Testing

Due Feb 15 at 11:59pm Points 100 Questions 6

Available until Feb 15 at 11:59pm Time Limit None

Instructions

A hypothesis test is a statistical test that is used to determine whether there is enough evidence in a

sample of data to infer that a certain condition is true for the entire population. A hypothesis test

examines two opposing hypotheses about a population: the null hypothesis and the alternative

hypothesis. Null hypothesis: usually refers to a general statement or default position that there is no

relationship between two measured phenomena, or no difference among groups. Alternative

hypothesis: opposite of the null; there is a difference or a relationship between measured phenomena

or groups

For example, a study is designed in order to test whether there is a difference in patient satisfaction

among two groups. Patient satisfaction is better in patients cared for solely by a registered nurse as

compared to patient satisfaction in patients cared for by a team of caregivers.

You are testing this hypothesis so let’s look at the null and alternative hypotheses.

Null Hypothesis: there is no difference in patient satisfaction. Alternative hypothesis: there is a

difference in patient satisfaction

In order to test this hypothesis, the researcher sets the level of significance (alpha). In most nursing

research the level of significance is set to .05. The number alpha is the threshold value that we measure

p values against. It tells us how extreme observed results must be in order to reject the null hypothesis

of a significance test. The alpha value gives us the probability of a type I error. Type I errors occur

when we reject a null hypothesis that is actually true (false positive). Type II errors occur when a

researcher fails to reject a false null hypothesis (a "false negative", i.e., rejecting a true hypothesis as

incorrect.

p-value: probability; every test statistic or hypothesis has a corresponding probability or p-value. This

value is the probability that the observed statistic occurred by chance alone, assuming that the null

hypothesis is true.

Statistical Significance

To determine if an observed outcome is statistically significant, we compare the values of alpha and the

p -value. There are two possibilities that emerge:

The p-value is less than or equal to alpha. In this case, we reject the null hypothesis. When this

happens, we say that the result is statistically significant. In other words, we are reasonably sure that

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Attempt History

Attempt Time Score

LATEST Attempt 1 1 minute 100 out of 100

Score for this quiz: 100 out of 100

Submitted Feb 11 at 3pm

there is something besides chance alone that gave us an observed sample.

The p-value is greater than alpha. In this case, we fail to reject the null hypothesis. When this happens,

we say that the result is not statistically significant. In other words, we are reasonably sure that our

observed data can be explained by chance alone.

Using the following chart, determine whether to accept or reject the null hypothesis and what type of

error may occur based on the p-values. Plac your answers in the multiple fill in the blank quiz. Use only

the words ACCEPT OR REJECT and I or II (Upper-case I (eye) only) in your answers. Do not add

the word "TYPE". Any other words or misspellings will be counted wrong. No late assignments

accepted in Week 5.

p-value alpha

null

hypothesis

potential error

1. .015 .01 accept or reject Type I or Type II

2. .01 .01 accept or reject Type I or Type II

3. .009 .01 accept or reject Type I or Type II

4. .061 .05 accept or reject Type I or Type II

5. .022 .05 accept or reject Type I or Type II

6. .05 .05 accept or reject Type I or Type II

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