Discussion work 1. Instruction here: At least 200 words for my discussion 2. Reply other post here: 100 words for reply Discussion response 1 1- When conducting quantitative research, you are attempting to answer a research question or hypothesis that you have set. One method of evaluating this research question is through a process called hypothesis testing, which is sometimes also referred to as significance testing.The first step in hypothesis testing is to set a research hypothesis.      In statistics terminology, the people in the study are the sample and the larger group they represent is called the population. For example, a sample of statistics students in a study are representative of a larger population of statistics students, you can use hypothesis testing to understand whether any differences or effects discovered in the study exist in the population. Hypothesis testing is used to establish whether a research hypothesis extends beyond those individuals examined in a single study.     Another example could be taking a sample of 200 breast cancer patients to test a new drug that is designed to eradicate this type of cancer. As much as you are interested in helping these specific 200 cancer patients, the real goal is to establish that the drug works in the population for all cancer patients.     In order to undertake hypothesis testing, the research hypothesis should be expressed as a null and alternative hypothesis. The null hypothesis and alternative hypothesis are statements regarding the differences or effects that occur in the population. The evidence is tested against the null hypothesis. When considering whether to reject the null hypothesis and accept the alternative hypothesis, consider the direction of the alternative hypothesis statement. The alternative hypothesis tells us two things. First, what predictions did we make about the effect of the independent variable(s) on the dependent variable(s)? Second, what was the predicted direction of this effect (Laerd Statistics, nd)?     A two-tailed prediction means a choice is not made over the direction that the effect of the experiment takes. It simply implies that the effect could be negative or positive. A one-tail prediction usually reflects the hope of a researcher rather than any certainty that it will happen.    If the statistical analysis shows that the significance level is below the set cut-off value (e.g., either 0.05 or 0.01), the null hypothesis is rejected and the alternative hypothesis is accepted. If the significance level is above the cut-off value, we fail to reject the null hypothesis and cannot accept the alternative hypothesis. You cannot accept the null hypothesis, but only find evidence against it.    Since the hypothesis is the question the researcher wants to answer, the clinical inquiry in healthcare, the research design, how the data is gathered and analyzed is determined by the question or hypothesis (Ambrose, 2018). In healthcare, we aim to fi ...

1. Discussion work
1. Instruction here: At least 200 words for my discussion
2. Reply other post here: 100 words for reply
Discussion response 1
1- When conducting quantitative research, you are attempting to
answer a research question or hypothesis that you have set. One
method of evaluating this research question is through a process
called hypothesis testing, which is sometimes also referred to as
significance testing.The first step in hypothesis testing is to set
a research hypothesis.
In statistics terminology, the people in the study are the
sample and the larger group they represent is called the
population. For example, a sample of statistics students in a
study are representative of a larger population of statistics
students, you can use hypothesis testing to understand whether
any differences or effects discovered in the study exist in the
population. Hypothesis testing is used to establish whether a
research hypothesis extends beyond those individuals examined
in a single study.
Another example could be taking a sample of 200 breast
cancer patients to test a new drug that is designed to eradicate
this type of cancer. As much as you are interested in helping
these specific 200 cancer patients, the real goal is to establish
that the drug works in the population for all cancer patients.
In order to undertake hypothesis testing, the research
hypothesis should be expressed as a null and alternative
hypothesis. The null hypothesis and alternative hypothesis are
statements regarding the differences or effects that occur in the

2. population. The evidence is tested against the null hypothesis.
When considering whether to reject the null hypothesis and
accept the alternative hypothesis, consider the direction of the
alternative hypothesis statement. The alternative hypothesis
tells us two things. First, what predictions did we make about
the effect of the independent variable(s) on the dependent
variable(s)? Second, what was the predicted direction of this
effect (Laerd Statistics, nd)?
A two-tailed prediction means a choice is not made over the
direction that the effect of the experiment takes. It simply
implies that the effect could be negative or positive. A one-tail
prediction usually reflects the hope of a researcher rather than
any certainty that it will happen.
If the statistical analysis shows that the significance level is
below the set cut-off value (e.g., either 0.05 or 0.01), the null
hypothesis is rejected and the alternative hypothesis is accepted.
If the significance level is above the cut-off value, we fail to
reject the null hypothesis and cannot accept the alternative
hypothesis. You cannot accept the null hypothesis, but only find
evidence against it.
Since the hypothesis is the question the researcher wants to
answer, the clinical inquiry in healthcare, the research design,
how the data is gathered and analyzed is determined by the
question or hypothesis (Ambrose, 2018). In healthcare, we aim
to find correlations and answers within the data to provide for
better patient population outcomes.
2- Hypothesis testing is a way of statistically testing meaningful
results or if the results happened by chance. The problem in a
hypothesis test is to decide to reject the null hypothesis in favor
of the alternative hypothesis.
There are two ways of testing the hypothesis, one-tailed test,
and two-tailed test (Ambrose, 2018). A one-tailed test is used
when a researcher is certain of the direction in which the data
will go. It includes right and left-sided tails depending on if the

3. alternative hypothesis is written as > or <. The two-tailed test is
used when researchers are looking to determine the differences
between the groups being compared. It is used when the
alternative hypothesis is written as not equal.
The testing hypothesis requires several steps. The first step
begins with expressing the hypothesis as a null or alternative
hypothesis. The null hypothesis is also called the false
hypothesis and is the currently accepted value for a parameter.
The alternative value is also called the research hypothesis and
involves the claims to be tested. The next step is to find the
likelihood of the sample result if the null hypothesis were true.
This probability is called the p-value. A low p-value would
indicate that the sample result would be unlikely if the null
hypothesis were true and would lead to the rejection of the null
hypothesis. A high p-value means that the sample results would
be likely if the null hypothesis were true and leads to the
retention of the null hypothesis. The use of the level of
statistical significance is used to determine where to draw the
line on making the decision how low a p-value should be for
rejection of the null hypothesis.
Finding critical values include the use of z-value or t-value. The
use of t- value includes a small population sample of less than
30. Critical values will also produce a region of rejection. The
next step is to find the test statistics. If the test statistic is
greater than the region of rejection, the null hypothesis is
rejected.
Hypothesis testing is important in determining if patient
education is effective in treating type 2 diabetics. The
researcher desires to know if patient education on type 2
diabetics makes a difference in the patient’s knowledge of type
2 diabetes. The null hypothesis is patient education does not
change the knowledge level of the participants. The alternative
hypothesis is that patient’s knowledge of type 2 diabetes
improves following patient education.

4. 3- Hypothesis testing is a way of statistically testing meaningful
results or if the results happened by chance. The problem in a
hypothesis test is to decide to reject the null hypothesis in favor
of the alternative hypothesis.
There are two ways of testing the hypothesis, one-tailed test,
and two-tailed test (Ambrose, 2018). A one-tailed test is used
when a researcher is certain of the direction in which the data
will go. It includes right and left-sided tails depending on if the
alternative hypothesis is written as > or <. The two-tailed test is
used when researchers are looking to determine the differences
between the groups being compared. It is used when the
alternative hypothesis is written as not equal.
The testing hypothesis requires several steps. The first step
begins with expressing the hypothesis as a null or alternative
hypothesis. The null hypothesis is also called the false
hypothesis and is the currently accepted value for a parameter.
The alternative value is also called the research hypothesis and
involves the claims to be tested. The next step is to find the
likelihood of the sample result if the null hypothesis were true.
This probability is called the p-value. A low p-value would
indicate that the sample result would be unlikely if the null
hypothesis were true and would lead to the rejection of the null
hypothesis. A high p-value means that the sample results would
be likely if the null hypothesis were true and leads to the
retention of the null hypothesis. The use of the level of
statistical significance is used to determine where to draw the
line on making the decision how low a p-value should be for
rejection of the null hypothesis.
Finding critical values include the use of z-value or t-value. The
use of t- value includes a small population sample of less than
30. Critical values will also produce a region of rejection. The
next step is to find the test statistics. If the test statistic is
greater than the region of rejection, the null hypothesis is
rejected.
Hypothesis testing is important in determining if patient

5. education is effective in treating type 2 diabetics. The
researcher desires to know if patient education on type 2
diabetics makes a difference in the patient’s knowledge of type
2 diabetes. The null hypothesis is patient education does not
change the knowledge level of the participants. The alternative
hypothesis is that patient’s knowledge of type 2 diabetes
improves following patient education.
4- Statistics is used to draw a conclusion about a population
with the data that has been collected. Once a hypothesis is
developed, data that has been collected from the sample
population is used to determine if the findings meet the
hypothesis. Studies are conducted using a specific sampling, but
that is different than developing a hypothesis which is
developed from an entire population.
A study determined that compassion in care has declined. The
study was able to “hypothesize that compassionate care is
beneficial for patients (better outcomes), health care systems
and payers (lower costs), and health care providers (lower
burnout)” (2017, Mazzarelli, Roberts, Trzeciak). They wanted
to establish compassionate care as an evidence-based practice.
It was determine that “compassion and human connection can
promote long-term resilience and well-being” (2017, Mazzarelli,
Roberts, Trzeciak) not only for the patient but for the health
care provider too.
A study determined that elderly patients who had worked as
caregivers retained a higher level of cognitive function as they
aged. “Fredman's team set out to test a common assumption that
stress among caregivers results in poorer health outcomes when
compared with non-caregivers” (2013, Hill). It was determined
that those who were caregivers performed at the level of a
younger age.
The Null Hypothesis is a to decide between two interpretations
of the statistical relationship in a sample during a study. The

6. null hypothesis suggests that there is relationship within the
sampling. If it can be determined that there is no relationship
between samples then the null hypothesis must be rejected and
use the alternative hypothesis.
The use of evidence-based practice has proved that patients
have a better outcome from their hospital stay. We use concepts
related to research such as side effects from medications,
expected outcomes from procedures, and safety such as
intentional rounding to prevent falls.
5.2 - Confidence intervals and hypothesis testing are similar as
they are both inferential methods that solely rely on
approximated sampling distribution. The confidence interval
represents a certain percentage of the survey from the sampled
population and they are often used with a margin of error. The
margin of error will indicate the uncertainty that surrounds the
estimated sample population. The researcher has a certain level
of confidence in mind that the results from the survey would
reflect what the researcher expected to find if in a case it was
possible for one to survey the entire population. In hypothesis
testing, there is an evaluation of the strength of evidence from
the sample population. These, therefore, show that hypothesis
testing and confidence interval are inferential methods that
purposely depend on the estimated sample population. The
clinical significance determines whether the research has a
practical application to an individual or group. With the use of a
confidence interval, the null hypothesis can be rejected from the
data obtained. The range of values determines the rejection in
that if the confidence value is 95%, it leads to the acceptance or
the rejection of the hypothesis. The confidence interval
estimates a parameter, and a hypothesis testing assesses the
evidence in the data against one claim and in favor of another.
Supposing a particular treatment reduced the risk of death
compared to placebo with an odds ratio of 0.5 and a 95%
confidence interval of 0,2 to 0.8. This means that, in the

7. sample, the treatment reduced the risk of death by 50%
compared to placebo, and the reduction risk lies between 20%
and 80%.
A study was carried out on the effects of controlled cord
traction in the third stage of labour on a postpartum
haemorrhage. The control treatment was a standard placenta
expulsion. Participants were women 18 years or older with a
single fetus at 35 or more weeks gestation and planned vaginal
delivery. The women in both groups were given prophylactic
oxytocin just after birth. The outcome was 2005 women
allocated to intervention ( controlled cord traction), 196 (9.8%)
experienced postpartum hemorrhage compared with 206 of 2008
allocated to control (10.3%). The reduction in risk of
postpartum hemorrhage associated with intervention was not
significant (relative risk 0.95; 95% confidence interval 0.79 to
1.15). The use of controlled cord traction had no significant
effect on the incidence of postpartum hemorrhage.
In a clinical trial of a new "wonder drug" for rheumatoid
arthritis, the remission rates turn out to be 5% higher in those
taking the new drug than in those taking the standard drug. The
null hypothesis will be Users of the new wonder drug are
statistically more likely to experience remission than users of
the standard drug. However, is a difference of 5% clinically
significant in a population of about 5,000 women? The
confidence interval range will determine whether the hypothesis
will be rejected or accepted.
An example of a workplace idea would be early ambulation in
postoperative patients prevents the risk of developing venous
thromboembolism (VTE). In a study, the rate of VTE was lower
among patients who could walk, when compared with those who
were immobile (10.6% vs 19.7%; p=0.03). The study showed
that patients with initial immobility who walked early were still
at risk of developing VTE. The risk can be reduced by the
combination of ambulation and administration of enoxaparin.

8. 6.2 - Hypothesis testing is the basis for all scientific research.
In health care, the use of hypothesis testing determines
improvements in patient outcomes The direction for conducting
a study is influenced by the hypothesis developed. The proposed
hypothesis needs to be tested and there are two ways for testing
the hypothesis. A hypothesis can be evaluated using a one-tailed
or a two-tailed test. One-tailed testing is the case when the
investigator is certain in which the data or the information will
go. Whereas in a two-tailed test, the investigator is looking to
identify the gaps between the groups that are being evaluated.
The importance of a two-tailed test is that here we can identify
the positive and negative impacts.
The confidence interval is a gap estimate for the mean.
These are a group of values that are kept close to the mean,
which can be either in a positive or negative direction. The
range of values decides the effect. The rejection and non-
rejection of the null hypothesis are based on the 95%
confidence interval. A 95% confidence interval means that the
research conducted will be 95% close to the true mean, but the
other 5% means that there a chance of 5 in 100 that the research
can go wrong. Confidence intervals are comprised of the point
estimate and a margin of error around that point estimate. The
margin of error indicates the amount of uncertainty that
surrounds the sample estimate of the population parameter. The
CI reflects the risk of the researcher being wrong. Reducing the
confidence interval increases the risk of error (Ambrose,2018).
Confidence intervals provide information about a range in
which the true value lies with a certain degree of probability, as
well as about the direction and strength of the demonstrated
effect. The size of the confidence interval depends on the
sample size and the standard deviation of the study groups. If
the sample size is large, this leads to "more confidence" and a
narrower confidence interval (Du Prel, Hommel, Rodrig &
Blettner,2009).
An example of the use of both hypothesis testing and
confidence interval can be seen in a study to determine the

9. impact and sustainability of the effect of chlorhexidine bathing
on central venous catheter-associated bloodstream infection.
Chlorhexidine bathing has been associated with reductions in
healthcare-associated bloodstream infection. Compared with
pre-intervention, during the active intervention, there were
significantly fewer central venous catheter-associated
bloodstream infections (6.4/1000 central venous catheter days
vs 2.6/1000 central venous catheter days, relative risk, 0.42;
95% confidence interval, 0.25-0.68; P<.001) (Monticalvo et al,
2012).