Consider the use of confidence intervals in health sciences with these articles as inspiration and insights.

Step 3: Using the data you collected for the Week 5 assignment (heights of 20 different people that you work with, discuss the method of collection of the values that you are using in your study (systematic sampling). What are some faults with this type of data collection? What other types of data collection could you have used, and how might this have affected your study?

Step 4:  Use the Spreadsheet to help you with calculations for the following questions/statements.

a) Give a point estimate (mean) for the average height of all people at the place where you work. Start by putting the 20 heights you are working with into the blue Data column of the spreadsheet. What is your point estimate, and what does this mean?
 

b) Find a 95% confidence interval for the true mean height of all the people at your place of work. What      is the interval? [ provide a screenshot below]

c) Give a practical interpretation of the interval you found in part b, and explain carefully what the output       means. (For example, you might say, “I am 95% confident that the true mean height of all of the   people in my company is between 64 inches and 68 inches”).

d) Post a screenshot of your work from the t value Confidence Interval for µ from the Confidence  table  Interval tab on the Excel spreadsheet

Step 5: Now, change your confidence level to 99% for the same data, and post a screenshot of this table, as well.

Step 6: Compare the margins of error from the two screenshots. Would the margin of error be larger or smaller for the 99% CI? Explain your reasoning.

Two references required

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To draw conclusions about a study population,
researchers use samples that they assume truly
represent the population. The confidence
interval (CI) is among the most reliable
indicators of the soundness of their assumption.
A CI is the range of values within which the
population value being studied is believed to fall.
CIs are reported in the results section of
published research and are often calculated
either for mean or proportion data (calculation
details are beyond the scope of this article).
A 95% CI, which is the most common level used
(others are 90% and 99%), means that if
researchers were to sample numerous times
from the same population and calculate a range
of estimates for these samples, 95% of the
intervals within the lower and upper limits of
this range will include the population value.

To illustrate the 95% CI of a mean value, say
that a sample of patients with hypertension has
a mean blood pressure of 120 mmHg and that
the 95% CI for this mean was calculated to range
from 110 to 130 mmHg. This might be reported
as: mean 120 mmHg, 95% CI 110-130 mmHg.
It indicates that if other samples from the same
population of patients were generated and
intervals for the mean blood pressure of these
samples were estimated, 95% of the intervals
between the lower limit of 110 mmHg and the
upper limit of 130 mmHg would include the true
mean blood pressure of the population.

Notice that the width of the CI range is a very
important indicator of how reliably the sample
value represents the population in question.
If the CI is narrow, as it is in our example of
110-130 mmHg, then the upper and lower limits
of the CI will be very close to the mean value of

Confidence interval: The range of values, consistent with the data, that is believed to encompass the actual or
“true” population value

Source: Lang, T.A., & Secic, M. (2006). How to Report Statistics in Medicine. (2nd ed.). Philadelphia: American College of Physicians

the sample. This sample mean value is probably a
more reliable estimate of the true mean value of
the population than a sample mean value with a
wider CI of, for example, 110-210 mmHg. With
such a wide CI, the population mean could be as
high as 210 mmHg, which is far from the sample
mean of 120 mmHg. In fact, a very wide CI in a
study should be a red flag: it indicates that more
data should have been collected before any
serious conclusions were drawn about the
population. Remember, the narrower the CI, the
more likely it is that the sample value represents
the population value. n

MAHER M. EL-MASRI, RN, PhD, IS AN ASSOCIATE PROFESSOR AND RESEARCH LEADERSHIP CHAIR
IN THE FACULTY OF NURSINg, UNIVERSITY OF WINDSOR, IN WINDSOR, ONT.

Confidence intervals: Part 1
TERMInoLogy 101

NurseONE resources
on THIS TopIc
EBSCO-MEDlInE full-text articles

• Hildebrandt, M., Vervölgyi, E., & Bender, R. (2009).
Calculation of NNTs in RCTs with time-to-event outcomes:
A literature review. BMC Medical Research Methodology,
9, 21.

• Hildebrandt, M., Bender, R., Gehrmann, U., & Blettner,
M. (2006). Calculating confidence intervals for impact
numbers. BMC Medical Research Methodology, 6, 32.

• Altman, D. G. (1998). Confidence intervals for the number
needed to treat. BMJ (Clinical Research Ed.), 317(7168),
1309-1312.

Myilibrary

• Campbell, M. J., Machin, D., & Walters, S. J. (2010). Medical
statistics: A textbook for the health sciences (4th ed).

• Mateo, M. A., & Kirchhoff, K. T. (Eds.). (2009). Research for
advanced practice nurses: From evidence to practice.

• Webb, C., & Roe, B. (Eds.). (2007). Reviewing research
evidence for nursing practice: Systematic reviews.

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STATISTICS IN HEALTH 6

These are the data set of 10 additional values in addition to the values provided in the question

.

60, 62, 64, 66, 66, 67, 68, 68, 70, 71, 72, 73, 74, 75, 75, 75, 76, 78, 78, 79. The screenshot of a spreadsheet is posted below.

The height is 65 inches. From the results, we can see that the sample mean height of the 20 values is 70.85 which is higher than me. It shows that my height is shorter than the sample mean. The sampling method that was used is systematic. Systematic sampling is where the participants are selected following a fixed and regular interval.This method is easy and unbiased. It means that the results will represent the actual population. During the study, I stood at the main gate entering the school between 9 am to 12 pm. The first twenty students entering the school formed my sample population. I took their heights with a stadiometer which I borrowed from my community clinic and recorded the values.

The study took place at the university entrance. The students were coming from all over New Jersey. Therefore, the sample resided from different locations. The age ranges of the participants involved school going teens with the ages ranging from 15 to 18 years. The study included both genders whereby out of the 10 participants, three were boys and seven were girls. The group included all races with no bias in terms of one’s racial affiliation. Everybody had an equal chance provided they come in that specified time of the study.

An empirical rule is a useful tool in determining where the 68th percent, 95th percent, and 99.7th percent values lie (Jaggia & Kelly, 2019). From the empirical values, 68% of the participants have heights between 65.30 and 76.4 inches. 95% of the participants have heights ranging from 59.75 to 81.94 inches. 99.7% of the participants have heights ranging from 54.2 to 87.49 inches.

After comparing the values from the normal distribution results, 0.4158% of participants are shorter than me, whereas 0.8542% of the participants are taller than me.

References

Jaggia, S., & Kelly, A. (2019). Business Statistics: Communicating with Numbers, 3e.

.

J

LU

TERMINOLOGY 101

Confidence intervals: Part 2
MAHER M. EL-MASRI, RN, PhD, IS AN ASSOCIATE PROFESSOR AND RESEARCH LEADERSHIP CHAIR
IN THE FACULTY OF NURSING, UNIVERSITY OF WINDSOR, IN WINDSOR, ONT.

Confidence interval: The range of values, consistent with the data, that is believed to encompass the actual or

“true” population value

Source: Lang, T.A., & Secic, M. (2006). How to Report Statistics in Medicine. (2nd ed.). Philadelphia: American College of Physicians

Part 1, which appeared in the February 2012
issue, introduced the concept of confidence
intervals (CIs) for mean values. This article
explains how to compare the CIs of two mean
scores to draw a conclusion about whether or
not they are statistically different. Two mean
scores are said to be statistically different if their
respective CIs do not overlap. Overlap of the CIs
suggests that the scores may represent the same
“true” population value; in other words, the true
difference in the mean scores may be equivalent

NurseONE resources
ON THIS TOPIC

EBSCO-MEDLINE FULL-TEXT ARTICLES

• Hildebrandt, M., Vervölgyi, E., & Bender, R. (2009).
Calculation of NNTs in RCTs with time-to-event
outcomes: A literature review. BMC Medical
Research Methodology, 9,21.

• Hildebrandt, M., Bender, R., Gehrmann, U.,
& Blettner, M. (2006). Calculating confidence
intervals for impact numbers. ß/MCMed/co/
Research Methodology, 6, 32.

• Altman, D. G. (1998). Confidence intervals forthe
number needed to treat. BMJ (Clinical Research
Ed.), 317(7168), 1309-1312.

MYÎLIBRARY

• Campbell, M. |., Machin, D., & Walters, S. I. (2010).
Medical statistics: A textbook for the health
sciences (4th ed).

• Mateo, M. A., & Kirchhoff, K. T. (Eds.). (2009).
Research for advanced practice nurses:
From evidence to practice.

• Webb, C, & Roe, B. (Eds.). (2007). Reviewing
research evidence for nursing practice:
Systematic reviews.

to zero. Some researchers choose to provide the
CI for the difference of two mean scores instead
of providing a separate CI for each of the mean
scores. In that case, the difference in the mean
scores is said to be statistically significant if its
CI does not include zero (e.g., if the lower limit is
10 and the upper limit is 30). If the CI includes
zero (e.g., if the lower limit is -10 and the upper
limit is 30), we conclude that the observed
difference is not statistically significant.

To illustrate this point, let’s say that we want
to compare the mean blood pressure (BP) of
exercising and sedentary patients. The mean BP
is 120 mmHg (95% CI 110-130 mmHg) for the
exercising group and 140 mmHg (95% CI
120-160 mmHg) for the non-exercising group.
We notice that the mean BP values of the two
groups differ by 20 mmHg, and we want to
determine whether this difference is statistically
significant. Notice that the range of values
between 120 and 130 mmHg falls within the CIs
for both groups (i.e., the CIs overlap). Thus, we
conclude that the 20 mmHg difference between
the mean BP values is not statistically
significant. Now, say that the mean BP is
120 mmHg (95% CI 110-130 mmHg) for the
exercising group and 140 mmHg (95% CI
136-144 mmHg) for the sedentary group. In this
case, the two CIs do not overlap: none of the
values within the first CI fall within the range
of values of the second CI. Thus, we conclude
that the mean BP difference of 20 mmHg is
statistically significant.

Remember, we can use either the CIs of two
mean scores or the CI of their difference to draw
conclusions about whether or not the observed
difference between the scores is statistically
significant. •

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However, users may print, download, or email articles for individual use.