1 2 6Strategic Communications PlanAysel Kara

1
2
6
Strategic Communications Plan
Aysel Karakas
Strayer University
COM510: Business Communications
Prof. Deborah Busby
Description
A. What is your challenge or opportunity?
Granite companies cannot calculate their cost and profits easily.
This program is a revolutionary software solution for stone
industry which provides the best end-to-end, user-friendly
software for the stone industry. The greatest challenge in this
task is trying to coordinate mission schedules with maintenance
availabilities to complete the installation.
B. Why is this professionally important to you?
This is my first opportunity to lead a portion of the project as a
new manager. Both the senior military and government
representatives along with my upper management will be
evaluating my proposals and choices as we move through the
project. Additionally, this is my first opportunity to show my
fellow project members that I am capable of leading them to
success.Goal
A. What goals or outcomes do you want to achieve with this
communication?
My goal is to minimize confusion of task responsibilities, pass
along the overall project goals, create good group

communication regarding mission and unit requirements, and
minimize delays.Audience
A. Who is your target audience?
The audience is the overall program manager for service team
projects, senior government, and military personnel and is
aimed towards the different subject matter experts (SMEs) that
will be performing the alteration and installation. Also included
will be the local Officers in Charge for all overseas locations,
as it will take a lot of planning and coordination with them to
find the mission downtime to complete the LAR.
I. What are the professional positions of the audience members?
a. Government Contract Core personnel
b. Valkyrie program manager
c. Unit OICs (Officers in Charge)
d. Overseas Deployment coordinator
e. OPTAR manager (Operating Target, Budget)
f. Electrician
g. Engine mechanics
h. Electronics communication technician
i. Welder
j. Quality assurance/NDT coordinator
k. Supply logistician
II. What demographic characteristics will the audience
comprise?
The demographics that cover this group vary greatly. There are
military personnel, white collar executives, and blue collar
office and shop personnel. Given the use of technology in
today’s workplace, all are well versed in communicating via
email.
III. What is your relationship to the audience?
I was previously the logistician for the shop but recently
completed my bachelor’s degree and have been promoted to a
Level 1 project manager. I have had constant contact with all
shop personnel along with the military management because of
the coordination of materials requirements.

IV. What background knowledge and expertise does the
audience have?
The service team mechanics and electricians have been working
on this class of patrol craft for over 10 years. Several of the
senior management for the military and Valkyrie are retired or
prior service personnel. The unit OICs deal with these craft
daily in an operational capacity. All have a basic knowledge of
the boat system requirements.
V. What does the audience know, feel about, and expect
concerning this communication?
The senior management believes that this is a necessary part of
the upgrade process. However, the majority of the shop workers
and unit commanders feel that they know the boats and what it
takes to work on them, so all they want are the schematics and
to go to work.
VI. What preconceptions or biases do you possess that might
prevent you from building rapport with your audience?
Having spent 17 years in the military along with the past 3
years working with the shop personnel, it will be difficult to
develop the fact that I am now a manager and responsible for
the overall performance.
B. What information is available about your audience?
· Preferred methods of communication
· Typical time-off patterns
· Types of humor they like
I. What research or sources will you use to obtain information
about the audience?
I plan to use my personal interactio ns with the audience
members over the past 3 years as my source of information.
II. What conclusions have you been able to draw about the
audience?
Since they all know these boats inside and out, it will be
difficult at times to keep their interest during any presentation.
C. What tone will you use to convey your message?

I. Is the setting casual or formal?
The setting will be formal. However, I will be trying to
encourage the participants to be casual and relaxed when
interacting about the topics.
II. Is the communication personal or impersonal?
The overall goal is make this communication personal. The
more I can make it relate to the shop personnel, the more they
will be willing to communicate. Additionally, the more personal
I make it to the senior executives, the greater flexibility they
will give me and the workers in getting the task completed.Key
Message
A. What is the primary message you must convey to your
audience?
This installation has many moving parts, but if we work
together and maintain open lines of communication, the task
will be accomplished with ease.
I. Is the message compelling and memorable?
The message is compelling because we have all worked on this
contract for several years and our ultimate goal is to support the
sailors using the boats.
II. Is the message clear and concise?
The message cannot get any clearer than we need to
communicate.
III. Is the message aligned with your audience’s goals and
needs?
Supporting the sailors and getting the job done right the first
time has always been our goal.Supporting Points
A. What three to four points, reasons, or justifications support
your message?
· Whether we are civilian employees or military personnel, the
goal is the end mission and that we need precise communication
for that outcome.
· Providing feedback and having open lines of communication
will allow us to quickly adapt to changes and challenges in the
upgrade process.
· The more information that can be gathered from each group

will allow for greater foresight into possible problems and
schedule issues.
I. What research or sources will you use to obtain facts and data
about your message?
I will be utilizing prior experiences on upgrades. Considering
the work packages of prior jobs will help to establish possible
obstacles we may face. Additionally, I will be utilizing the
Navy manuals that support maintenance and upgrades to ensure
all personnel are provided with the proper procedures.Channel
Selection
A. What communication style will you employ (tell/sell or
consult/join) and why?
The initial communication style that I will be shooting for is
consult/join. Since there is not much that can be learned from
me other than the type of upgrade that will be performed, the
consult/join style will allow me to learn from the shop workers
what they will need and what possible challenges they expect. I
may need to utilize the tell/sell method down the line when we
hit obstacles and I need to convince the upper chain of
command what direction we should go (Munter, 1).
B. What channels will you use to deliver your message, and why
will they be the most effective?
C. What purpose is served by each style and channel you have
selected?
I. PowerPoint presentations—this will allow for the
incorporation of picture as well as videos to illustrate the
changes to be performed. Additionally, it will interface with
video conferencing.
II. Microsoft net meeting or similar software to connect to
offices overseas and across the country during the presentation
so everyone can be on the same page. This can also serve as a
good supporting action to what types of communication are
expected and to the scope of the project.
III. Email updates will be sent out as required to keep everyone
informed of the progress.Action Request

A. What is your action request?
B. Is the action request you are making to your audience clear,
concise, and easily actionable?
The call to action to maintain open lines of communication up
and down the chain of command and to identify the task
required by each group of employees is very clear and easil y
actionable.
Sources
1. Mary Munter. 2014. Guide to Managerial Communication.
https://strayer.vitalsource.com/#/books/9780133558050/
A power analysis is a statistical procedure needed to determine
an effective sample size to make a reasonable conclusion.
Power Analyses (Ali & Bhaskar, 206; Polit & Beck, 2017)
Helps to decide how large a sample is needed to make sure
judgments about statistical findings are accurate and reliable.
Prevent the recruitment of too many or too few numbers of
subjects.
Must be used to determine sample size before the study begins
Chi-Square is powerful for what it is intended to do – determine
if variables are associated in any way.
Chi-Square Analysis (Ali & Bhaskar, 206; Polit & Beck, 2017)
Is a type of inferential statistic.
Evaluates if two categorical variables (e.g., gender, educational
level, race, etc.) are related (correlated) in any way.
Appropriate for discrete variables (nominal, ordinal).
Does not work with continuous variables (interval, ratio).
The null hypothesis states that there is no relationship between
variables. As such, if the null is accepted, you are agreeing that
there is no relationship between variables.
Null Hypothesis Testing (Polit & Beck, 2017)

The beginning point for statistical significance testing.
A formal approach to deciding between two interpretations of a
statistical relationship in a sample.
Null Hypothesis – suggests there is no relationship between
variables, populations, etc. meaning there was an error in
sampling.
Alternative Hypothesis – suggests there is a relationship
between variables, populations, etc.
Rejecting the Null Hypothesis – suggests being in support of the
Alternative Hypothesis.
I want to pick up on your comments specific to type I and type
II error and add some thoughts because it is very important to
understand these concepts.
First, I can share that sometimes as a researcher, making sense
of a type I and type II error can be really challenging! It is
important to understand what each is as well as the strategies
researchers use to prevent these errors.
In statistics, a null hypothesis is a statement that one seeks to
nullify (that is, to conclude is incorrect) with evidence to the
contrary. Most commonly, it is presented as a statement that the
phenomenon being studied produces no effect or makes no
difference. An example of such a null hypothesis might be the
statement, "A diet low in carbohydrates has no effect on
people's weight." A researcher usually frames a null hypothesis
with the intent of rejecting it: that is, intending to run an
experiment which produces data that shows that the
phenomenon under study does indeed make a difference (in this
case, that a diet low in carbohydrates over some specific time
frame does, in fact, tend to lower the bodyweight of people who
adhere to it)
A type I error(or error of the first kind) is the incorrect

rejection of a true null hypothesis. Usually, a type I error leads
one to conclude that a supposed effect or relationship exists
when in fact it doesn't. Examples of type I errors include a test
that shows a patient to have a disease when in fact the patient
does not have the disease, a fire alarm going on indicating a fire
when in fact there is no fire, or an experiment indicating that
medical treatment should cure a disease when in fact it does
not. The type I error rate or significance level is the probability
of rejecting the null hypothesis given that it is true. It is
denoted by the Greek letter α (alpha) and is also called the
alpha level. Often, the significance level is set to 0.05 (5%),
implying that it is acceptable to have a 5% probability of
incorrectly rejecting the null hypothesis.
Type I error is rejecting the null hypothesis when it is true. This
is also known as a false positive finding or conclusion.
A type II error(or error of the second kind) is the failure to
reject a false null hypothesis. Examples of type II errors would
be a blood test failing to detect the disease it was designed to
detect, in a patient who really has the disease; a fire breaking
out and the fire alarm does not ring, or a clinical trial of a
medical treatment failing to show that the treatment works when
really it does. The rate of the type II error is denoted by the
Greek letter β (beta) and related to the power of a test (which
equals 1−β).
A type II error is the failure of rejecting a false null hypothesis.
This is also known as a false negative finding or conclusion.
Much of statistical theory revolves around the minimization of
one or both of these errors, though the complete elimination of
either is treated as a statistical impossibility.
sampling.
One of the things that are significant to consider is the use of a
sample and sampling error. We all generally understand that a
sample, be it randomized or a convenience method, is intended
to represent a certain population. Inferences about sample
statistics (such as a mean and standard deviation) are used to

approximate population parameters. Martin and Bridgmon
(2012) posit it is highly unlikely that sample statistics will be
exactly the same as population parameters and the difference
that emerges is what is referred to as sampling error.
It is important to remember as you move forward in your
doctoral program, that the use of random sampling is thought to
be the best way to reduce sampling error (Martin & Bridgmon,
2012; Tappen, 2016: Treiman, 2009). However, Shadish et al.
(2002) have previously discussed that an alternative selection
method to random selection used by experimental researchers is
known as purposive sampling. This is an intentional process by
a researcher to identify population characteristics, including
participants, treatments, outcomes, and settings, and then select
a sample that most closely embodies the desired population
characteristics. However, I encourage you to carefully consider
that random error can occasionally include fluctuations in the
characteristics of individuals, such as the motivation level of
novice nurses changing rapidly from one intervention to the
next. So the timing, or rather, the length of study involving the
participants, must always be key part of the methodology
design.
Thank you for your post this week that includes an important
dialogue about the null hypothesis. As pointed out by
MacKenzie et al. (2018), hypothesis testing leads to a
dichotomous decision; one either rejects the null hypothesis or
not. They also argue the decision by the researcher (to reject or
not) can be either right or wrong with respect to the truth.
However, it is important to gently remember that a hypothesis
can never be “proven” to be correct. I have heard my previous
doctoral students describe their findings in this way and this
description is inaccurate. We should always be mindful that no
amount of experimental testing or evidence can truly prove
anything in science (McLaughlin, 2006). Rather, it is important
to note that if a research scientist’s results match his/her

prediction, then the hypothesis is supported.
The language we use to describe possible problems with a
hypothesis is also key. The nomenclature for the errors that can
occur, such as Type 1 or Type 2, depend upon the decision of
whether to reject the null hypothesis. Other considerations
involve the p value and its role in determining to accept or
reject the null hypothesis. There are some key factors to
remember from the lesson this week on the p value (Taylor,
2019). These include:
With most analyses, an alpha of 0.05 is used as the cutoff for
statistical significance;
If the p-value is less than 0.05, we reject the null hypothesis
that there is no difference between the means and conclude that
a significant difference does exist;
If the p-value is larger than 0.05, we cannot conclude that a
significant difference exists;
The p-value is also known as the calculated probability; and
The p-value is commonly referred to as the alpha.
If the null hypothesis is not true, then the effect size will be
greater than zero;
The larger the effect size, the greater the degree to which the
phenomenon (e.g., the outcome of an intervention study) will be
manifested; and
The larger the effect size is, the greater the power will be, so a
smaller sample will be needed to detect the phenomenon and
reject the null hypothesis (Cohen, 1988) [as cited by Tappen,
2016, p. 128].
I also took particular note of your comments about whether you
would utilize the research study with regard to the suggested
error. You explained that there are still too many gray areas to
feel a comfortable in considering this particular research. I can
see certainly see your point about this but want to offer that one
of the potential opportunities is for the study to be repeated by

other researchers, making note of the contributing factors which
contributed to the original sample error. One of the challenges
with the case study this week is that the generalizability of the
results cannot be considered until the sample error is resolved.
Thank you and take care.
the Chi-square test of independence (also known as the Pearson
Chi-square test, or simply the Chi square) is one of the most
useful statistics for testing hypotheses when the variables are
nominal, as often happens in clinical research (McHugh, 2013).
Chi Square is used to examine the difference between what you
actually find in your study (or DNP Project) and what you are
expecting to find. So, there are key questions which should be
asked to determine if this test is appropriate for your upcoming
DNP Project which include:
Are you trying to see if there is a difference between what you
have found and what would be found in a random pattern?
Is the data gathered organized into a set of categories?
In each category, is the data displayed as frequencies (not
percentages)?
Does the total amount of data collected (observed data) add up
to more than 20?
Does the expected data for each category exceed four?
If any of the answers are no to these questions, then is not the
correct test to use. Thanks Angela, and I hope this information
is helpful. Take care.
Kindly,
Dr. Kyzar
Reference
McHugh, M.L. (2013). The chi-square test of independence.
Biochemia Medica, 23(2), 143-149.
http://dx.doi.org/10.11613/bm.2013.018 (Links to an external

site.)
ReplyReply to Comment
Collapse SubdiscussionKate Blue
Kate Blue
WednesdayJan 27 at 4:56pm
Dr. Kyzar and class,
What statistical procedure is needed to determine an effective
sample size to make a reasonable conclusion? Explain your
rationale.
When determining the appropriate sample size, the general
rule of the have the largest sample size possible is best. This
allows for the best representation of the population. Utilizing
the power analysis tool helps prevent type II errors or false
negatives. In conducting this test, the validity of the conclusion
is stronger (Polit, 2016).
Reading through the study, you observe that the researcher used
a chi-square analysis to analyze nominal and ordinal data. Is
this the appropriate level of statistical analysis to answer the
research question? Explain your rationale.
Chi-square analysis determines the differences in variables
and is used to test the hypothesis. It can be utilized in both
nominal and ordinal data (Chamberlain College of Nursing,
2020). The observed frequencies of outcome following
intervention are compared to the expected frequencies of
outcome following not intervention. Through calculations, a
0.05 significance is determined. Then the Chi-square value and
significance value are compared leading to the p-value, which
determines statistical significance. In most nursing research, a
value of 0.05 or less is considered to be statistically significant
(Polit, 2016).

Reading further, the researcher reports that the p-level led her
to conclude that the null hypothesis was rejected. In your
critique of the study, you determine that the null hypothesis is
true. Do these findings impact your decision about whether to
use this evidence to inform practice change? Why or why not?
When the p level is greater than 0.05 it is determined to be
not statistically significant. It means more than five out of one
hundred times of completing a similar size study the results
would show probability of the control versus intervention
groups being different by likelihood. If the DNP scholar found
a p level less than 0.05 it would be considered statistically
significant and support the null hypothesis (Polit, 2016).
When a researcher rejects a null hypothesis that is true a type
1 error occurs. When the criteria for type 1 is very tight there
is a higher chance for type 2 errors. To avoid type 2 errors the
researcher wants to have a larger sample size. This would
change the DNP scholar’s choice to utilize in practicum. When
there is an error it may be related to inadequate sample size,
poor statistical processes, inaccurate measures, or errors in the
study method (Polit, 2016). Therefore, the level of evidence
would be poor leading to a non-acceptable study for
implementing practice change.
References
Chamberlain College of Nursing. (2020). NR 714 Week 4: Data
Analysis [Online lesson]. Adtalem Global Education.
Polit, D. F. (2016). Nursing research: Generating and assessing
evidence for nursing practice (10th ed.). Wolters Kluwer
Health.
Excellent comments to this week’s post! You have provided a

highly competent discussion of the Week 4 questions and I took
particular note of your points about the Chi Square Analysis.
Since this is one of the main focuses of our study this week, I
want to summarize some very important key points about this
important test. Below are a list of advantages and disadvantages
that students should remember.
Advantages
Noted for its robustness with respect to distribution of the data
and ease of computation;
The detail of information which can be derived from the test;
It use in studies when certain parametric assumptions cannot be
met;
Its flexibility in handling data from both two groups and
multiple group studies;
Can be used to test the difference between an actual sample
(actual data) or observed, and hypothetical expectations
(expected outcome/values from a hypothesis);
Can test association between variables;
Used to test the difference between what you expect to find
from an experiment, and what you actually find from an
experiment; and
Examines the differences in data between categories.
Disadvantages
Can be used on raw data that is counted (BUT cannot be used
for measurements, proportions, or percentages);
Data must be numerical;
Categories of two are not good to compare;
The number of observations must be 20+;
The test becomes invalid if any of the expected values are
below 5; and
A type of nonparametric statistics and is not as powerful as
other types of statistical methods.
Thanks so much and I hope this information greatly adds to
your knowledge this week about this important test.

Kindly,
. Additionally an estimate of a sample size is used to avoid a
type 2 error which happens when the null hypothesis is accepted
when it should be rejected (Polit & Beck, 2017, p 402).
These comments are at the heart of a power analysis, which is a
method of estimating that the sample is large enough to assume
that your statistical analysis is meaningful and large enough to
detect errors. An underpowered quantitative study typically has
too few subjects or an insensitive measure of change (or both),
leading to an increased risk of type 2 errors. The effect size is
another important estimate of power and is the estimated
magnitude of the phenomenon under study (Tappen, 2016, p.
127). Consider then, that if the null hypothesis is true, the effect
size will be zero; however, most researchers anticipate a real
effect in most of their studies. Thus, it is important for the
researcher to actually conduct a real calculation of the sample
population which will be needed.
I can share that there are some very useful software packages
that can calculate power for you (many are free on the internet),
but some of these may be limited by the number of designs that
can be inputted into the calculation in question (Creswell &
Creswell, 2017). Ultimately, the best advice for doctoral
students and novice researchers is to use as large of a sample as
possible to maximize the possibility that the means,
percentages, and other statistics are the true estimates of the
population (Wood & Ross-Kerr, 2011, p. 129
Please click on the link below and read the brief discussion.
Knowing the content this week may be a bit challenging, does
the illustration help solidify your understanding of rejecting the
null hypothesis (meaning a difference exists) or failing to reject
the null (meaning no difference was found)?

Best Regards,
Dr. Kyzar
https://www.statisticssolutions.com/to-err-is-human-what-are-
type-i-and-ii-errors/ (Links to an external site.)
Rejecting the Null Hypothesis--Week 4.jpg
Collapse SubdiscussionCarlos Legra Elias
Carlos Legra Elias
SaturdayJan 30 at 12:55pm
Hi Dr Kyzar
Excellent to know how rejecting and failing to reject null
hypothesis The illustration explains the concept of Type I and
Type II error that can result from rejecting and failing to reject
the null hypothesis (Davis, 2020). Type I error occurs when a
true null hypothesis is rejected. Type II error on the side occurs
when we fail to reject a false hypothesis. The rejection region
as illustrated depends on the significant level provided. A
significant level of α = 0.05 is commonly used in many research
studies. However, other significant levels such as α = 0.01 and
α = 0.10 can also be used depending on the requirement and
type of the study. Using one of the significant levels affects the
decisions made on the null hypothesis. A calculated p-value can
be statistically significant when α = 0.10 but fails to be
significant when the significant level is changed to α = 0.05.
This study helps in illustration of hypotheses testing during data
analysis. This is one of the most vital part that can lead to
decision making using the provided data. It is clear from the
given illustration that decisions made have a great impact and
the type of error committed can be costly depending on the
study at hand. This explains the importance of keenness when

handling hypotheses.
Therefore, the article solidifies the understanding of rejecting
the null hypothesis and failing to reject the null hypothesis. It
helps in understanding how irrelevant a decision can be when
either of the errors is made.
Reference
Davis, I. (2020, March 4). To Err is Human: What are Type I
and II Errors? Retrieved January 30, 2021, from
https://www.statisticssolutions.com/to-err-is-human-what-are-
type-i-and-ii-errors/ (Links to an external site.)
Collapse SubdiscussionJannet Patton
Jannet Patton
Greetings, Dr. Kyzar and Class,
Thank you for providing the additional resources and questions
this week to further our understanding of Type I and Type II
errors. I especially appreciated the article from Statistics
Solution
s and the illustration about holding versus looking at puppies to
demonstrate the difference between a Type I and Type II error.
For example, if a researcher rejects a true null hypothesis, this
would be a Type I error-also known as a false-positive. If the
researcher fails to reject a false null hypothesis, this would be

considered a false-negative result (McLeod, 2019). Type I
errors can cause unnecessary problems when making healthcare
changes, and Type II errors can prevent needed interventions
(McLeod, 2019). I have attached a visual chart that
demonstrates the conclusion from statistical analysis, based on
whether the results yielded a Type I error or Type II error
(McLeod, 2019).
Jan
Reference
McLeod, S. A. (2019). What are type I and type II errors?
Simple Psychology.
https://www.simplypsychology.org/type_I_and_type_II_errors.h
tml (Links to an external site.)
McLeod_Chart.docx
Collapse SubdiscussionRacheal Aneke
Racheal Aneke
WednesdayJan 27 at 9:19pm
NR 714 week 4 discussion

rationale.
To determine if the sample size is adequate to make a
reasonable decision depends on the descriptive statistics applied
in the study like mode, median, and mean. Mode occurs with the
most significant mathematical values frequency and measures
the nominal data in a central tendency. The median score is at
the 50th percentile or center of the frequency or distribution. It
is the correct measurement of the ordinal data. Mean is the sum
of the total score divided; it measures the interval and ratio.
Another statistical method to decide a sample size includes the
range, variance, and standard deviation. Chi-test is also a
statistical procedure that is needed to determine sample size
(Chamberlain College of Nursing. (2021). Facts remain that
there might be enough participants in a research study and
statistical methods to select the sample size. The sample size in
quantitative research studies is sometimes invariable, numerical
at the beginning of the study, or the end as a narrative (Gray &
Grove, 2021). One numerical technique does not decide a
sample size in a study.
chi-square analysis to analyze nominal and ordinal data. Is this
the appropriate level of statistical analysis to answer the

The Chi-square test is the statistical procedure that describes
the different variables in a research study, such as if they are
dependent or independent variables. It determines the
descriptive measurements and can use nominal or ordinal data,
but not for causalities. Since this is the case, the Chi -square
analysis is acceptable to use to answer the research question.
Chi-square results are not significant if there is no difference in
the outcome, but the researcher using chi-square implement
numerous samples (Chamberlain College of Nursing, 2021). But
if the product is positive, it is applied. My research question
can be answered with chi-square because it assesses the
mentally ill in a crowded environment.
Type II error regarding null hypotheses occurs because of
mistakes in the research methods, but they are false positives.
Type II error risk is decided using power analysis. Power

analysis includes the level of significance, sample size, power,
and effect size as parameters (Polit & Beck, 2017). As long as
the minimum power level is obtained for the study, the validity
is more robust, and the study is less questionable. Based on the
fact that if a research null hypothesis is rejected, the power
level analysis is reported to question the study's outcome
validity, I would not change my decision to use it as evidence to
inform practice change.
References
Chamberlain College of Nursing. (2021). NR-714 week 3:
Summaries of multiple Research studies Address the Systematic
Review.[Online lesson]. Adtalem Global Education.
Gray, J., & Grove, S. K. (2021). Burns and Grove's the practice
of nursing research: appraisal, synthesis, and generation of

evidence. Elsevier.
Polit, D. F., & Beck, C. T. (Eds.). (2017). Nursing research:
Generating and assessing
evidence for nursing practice (10th ed.). Wolters Kluwer. 
Collapse SubdiscussionTheresa Kyzar
Theresa Kyzar
FridayJan 29 at 6:17pm
Hi Racheal:
Thank you for your practical commentary to the Week 4
discussion. I noted the macro-level perspective you provided as
part of a general overview of statistical methods and the chi
square test. However, I want you to take a deeper dive with me
because it is important to really grasp the concepts associated
with parametric and non-parametric testing. Remember that you
will likely be required during your defense presentation of your
DNP project to discuss your data analysis to demonstrate
mastery of the statistical tests you utilized to measure the
outcomes of your change project. It will be exciting, I promise!

So, let’s talk about this more at the doctoral level now so you
will be able to explain it during your final DNP Project
presentation. As you have learned from your assigned chapter
readings this week, the Chi-square test is a non-parametric
statistic, also called a distribution free test (Polit & Beck,
2017). From this important information, you (and your fellow
student peers) have learned that non-parametric tests should be
used when any one of the following conditions pertains to the
data:
The level of measurement of all the variables is nominal or
ordinal.
The sample sizes of the study groups are unequal; for the χ2 the
groups may be of equal size or unequal size whereas some
parametric tests require groups of equal or approximately equal
size.
The original data were measured at an interval or ratio level,
but violate one of the following assumptions of a parametric
test:
The distribution of the data was seriously skewed or kurtotic
(parametric tests assume approximately normal distribution of
the dependent variable), and thus the researcher must use a
distribution free statistic rather than a parametric statistic.
The data violate the assumptions of equal variance or

homoscedasticity.
For any of a number of reasons, the continuous data were
collapsed into a small number of categories, and thus the data
are no longer interval or ratio (McHugh, 2013).
Sometimes the term, homoscedasticity, scares students, but
really it shouldn’t. It just means “having the same scatter”. As
an example, we want our data results from the participant
responses to a questionnaire used as part of a DNP project or
research study to be scattered evenly on a plot graph. This is
very important if I am trying to compare the responses between
two groups who participated (such as physician responses vs
nurse responses). However, both groups need to be the same
sample size or interpretation of the data, especially for
comparison, could be compromised. Does that make sense?
Look at the example below:
Homoscedasticity Example.jpg
What about Groups B & C? Is it the same, meaning, evenly
scattered? The answer is no, likely because the two groups in
the project were not the same sample size (say as an example,
10 nurse and 6 physician participants). So, I cannot say
determine an association in the data (among participant
responses) and that is a problem. This is where the important
use of non-parametric testing comes into play and I will likely

need to use a chi square test analysis. The exciting aspect of
this particular test is its flexibility in handling data from both
two groups and multiple group studies and I can then delve into
a possible association between the two sample groups.
Does this information help to better explain how nonparametric
testing, including a chi square test can support your upcoming
DNP project data analysis? Let me know your thoughts. Thanks!
Kindly,
Dr. Kyzar
References
McHugh, M.L. (2013). The chi-square test of independence.
Biochemia Medica, 23(2), 143-149.
http://dx.doi.org/10.11613/bm.2013.018 (Links to an external
site.)
Ogee, A., Ellis, M., Scibilia, B., & Pammer, C. (2021).
Homoscedasticity? Don't be a victim of statistical
hippopotomonstrosesquipedaliophobia.
https://blog.minitab.com/blog/statistics-and-quality-data-
analysis/dont-be-a-victim-of-statistical-

hippopotomonstrosesquipedaliophobia (Links to an external
site.)
Polit, D. F., & Beck, C. T. (2017). Nursing research: Generating
and assessing evidence for nursing practice (10th ed.). Wolters
Kluwer.
, R. (2016). Advanced nursing research: From theory to practice
(2nd ed). Jones & Bartlett.
Edited by Theresa Kyzar on Jan 29 at 6:21pm
Racheal Aneke
SundayJan 31 at 8:36am
Dr. Kyzar,
Thank you so much for those in-depth explanations on how to
solve homoscedasticity problems with non-parametric testing
like chi-square.
Yes, it was and will be more helpful on this DNP Journey.
I appreciate your support,

Racheal Aneke
Collapse SubdiscussionKimberly Austin
Kimberly Austin
ThursdayJan 28 at 5:36pm
As a practice scholar, you are searching for evidence to
translate into practice. In your review of evidence, you locate a
quantitative descriptive research study as possible evidence to
support a practice change. You notice the sample of this study
includes 200 participants and is not normally distributed.
Reflect upon this scenario to address the following.
rationale.
Research typically relies on samples for their data. Because of
the importance of the accuracy of data collected, researchers
must carefully choose their samples. Samples should allow the
researchers to draw conclusions with good validity and pertain
beyond the sample used (Polit & Beck, 2017).
A sampling plan identifies how subjects are selected and how
many subjects should be used for the research. In the first step,
researchers should clearly identify the criteria that defines who

is in the population, and if it represents all types of individuals
and diversity for similarly occurring circumstances. This
eligible criteria composes the inclusion or exclusion criteria
needed for the study. Additional considerations should include
costs, constraints, individual ability to participate, and design
considerations (Polit & Beck, 2017).
The statistical procedure needed to determine an effective
sample size is power analysis. Power analysis is best used with
larger sample groups to decrease the likelihood of achieving a
deviant sample (Polit & Beck, 2017). In the scenario above, the
study includes 200 participants therefore power analysis would
be best for this quantitative study as this sample will have an
increased likelihood of being representative, however the large
sample does not guarantee this (Polit & Beck, 2017).
a chi-square analysis to analyze nominal and ordinal data. Is
this the appropriate level of statistical analysis to answer the
The Chi-Square Test determines if two variables are
independent or related. This case is typically used with nominal
or ordinal data, and results used when the analysis shows a
difference (Chamberlain, 2020).

According to McHugh (2013) the Chi-square test is a non-
parametric statistic, or distribution free, test, therefore is
appropriate for use in the study reviewed above.
Polit and Beck (2017) explain that hypothesis testing is based
on negative inference. When differences occur from random
factors, it is known as the null hypothesis. When this occurs,
care must be taken to not jump to quickly on how to use these
results. When deciding to accept or reject a null hypothesis,
researchers, must determine how probable that chance was the
factor causing the results. Rejecting a true hypothesis, or
accepting a false hypothesis are two types of statistical errors
researchers can make, known as Type I and Type II errors (Polit
& Beck, 2017).
In the scenario above, the researcher concluded the null
hypothesis was rejected, however upon my further review, the
null hypothesis is true, therefore the researcher made a Type I
error; and I would use these findings to support the practice
change.

References
Chamberlain College of Nursing. (2020). NR 714 Week 4: Data
Analysis [Online lesson]. Adtalem Global Education.
McHugh M. L. (2013). The chi-square test of independence.
Biochemia medica, 23(2), 143–149.
https://doi.org/10.11613/bm.2013.018
Kluwer Health.
Collapse SubdiscussionKayla Jones
Kayla Jones
SundayJan 31 at 12:10pm
Hi Kimberly,
Thank you for further explanation in simplest terms for this

week’s discussion! Dreaded by statistics, it has been insightful
to review the readings in detail along with reading everyone’s
posts! While many of us may not communicate the statistical
terminology in talking to others, it is used in our everyday life.
Hypotheses are basically our guesses of why and how events are
occurring. In many instances, our guesses are inspired by our
beliefs and experiences (Kaur, 2017). However, in research the
hypothesis is the relationship between the independent and
dependent variable. The independent variable is the cause with
the dependent variable being the effect. Hypotheses play a
different role in qualitative and quantitative research.
Quantitative research stimulates critical thinking, engaging the
researcher to interpret the data. Qualitative research usually
does not include the hypothesis in the early stages because
observation and detailed interviews/inquiries are the driving
forces to justify the hypothesis (Kaur, 2017). Concluding the
research, a significance level is established to test the
hypothesis in which can further determine the reliability. In
agreement with you, it is my belief that it would be safe to
implement the change.
Reference
Kaur, S. P. (2017). Writing the hypothesis in research.
International Journal of Nursing Education, 9(3), 122-125.

https://doi:10.5958/0974-9357.2017.00081.2Links to an external
site.
Theresa Kyzar
Hi Kim,
Thank you for your contribution to the week 4 discussion!
A key point to remember from this discussion relates to power
analysis. Please allow me to build on your post.
A power analysis is a statistical procedure needed to determine
an effective sample size to make a reasonable conclusion.
Researchers conduct a power analysis before recruiting human
subjects into their quantitative research study. In each
published quantitative study, the research team will state a
power analysis as well as report what it was a priori.
Help decide how large a sample is needed to make sure

subjects.
The cut-off for a power analysis is 0.80.
Must be used to determine sample size beforethe study begins
Kindly,
Dr. Kyzar
References
Ali, Z., & Bhaskar, S. B. (2016). Basic statistical tools in
research and data analysis. Indian Journal of Anesthesia, 60(9),
662–669. doi:10.4103/0019- 5049.190623. Retrieved
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5037948/
(Links to an external site.).
Polit, D.F. & Beck, C. T. (2021). Nursing research: Generating
and assessing evidence for nursing practice (11th ed). Wolters
Kluwer.
Collapse SubdiscussionCarlene Yearwood
Carlene Yearwood

With regards to the sample size of 200 participants and not
normally distributed, the statistical procedure needed to
determine an effective sample size for a reasonable conclusion
is the P value or alpha level, power analysis, effect and
alternative hypothesis. Sample size is a critical step in the
design of a well - planned research protocol (Polit & Beck,
2017). P values states whether an observation is as a result of a
change made or random occurrences. In order to accept the
result there must be a low - p value. Studies must have an
adequate sample size, relative to the goals and variabilities of
the study (Suresh, et, al. 2012).
Chi- square analysis is used to analyze nominal and ordinal
data. The chi- square test analysis is one of the most useful
statistics for testing hypotheses when the variables are nominal
in clinical research (Mc Hugh, 2013). This is the appropriate
level of statistical analysis to answer the research question
since chi- square can provide information not only on the
significance of any observed differences, but also provides
detailed information on exactly which categories account for
any differences found. The amount and detail of information
this statistical analysis can provide makes it one of the most

useful tools available to researchers for analysis ( Mc Hugh,
2013).
Depending on the p - value, a low p - value means the sample
result would be unlikely if the hypothesis were true and leads to
the rejection of the null hypothesis. In null hypothesis testing
the criterion is called a (alpha) and is almost set to .05. If there
is enough evidence to conclude that the null hypothesis is true, I
will use the evidence to inform practice change. Researchers
can only conclude that hypotheses are probably true or false and
there is always a risk for error ( Polit & Beck, 2017).
Researchers can make a Type 1 error by rejecting a null
hypothesis that is true.
References
Mc Hugh, M., L. ( 2013). The Chi - square test of independence.
Biochemia Medica, 23 ( 2), 143 - 149.
Polit, D.F. & Beck, C. T. (2017). Nursing research: Generating
Kluwer.
Suresh, K. P. & Chandrashekara, S. (2012). Sample size
estimation and power analysis for clinical research studies.

Journal of Human Reproductive Sciences, 5 ( 1), 7 - 13.
Collapse SubdiscussionEjiro Ohonbamu
Ejiro Ohonbamu
Carlene thanks for your post. In other to get a better
understanding, of the p-value. You stated that the researchers
can make a Type 1 error by rejecting a null hypothesis which is
true. The null hypothesis in many types of research is typically
not voiced, but in this case, it is. Now to determine the obvious
that the null was actually true. The null hypothesis is truthfully
based on statistical errors that precede further investigation.
Prior to any conclusion, do you not think that what led to the
initial rejection must be investigated? Will you be using the
evidence for the rejection to inform the practice change? I am
thinking that the evidence that led to the rejection, is the same
evidence that has led to the truth. The higher the level of
significance, the more extreme, nature of the risk of a Type I
error diminishes (Polit & Beck, 2017). Thanks for your post.
References

Theresa Kyzar
Week 4 Class Question
Students:
What is a normal distribution and what does it look like? Why
is the normal distribution important in statistics?
I look forward to your responses. Thank you!
Dr. Kyzar
Collapse SubdiscussionAngela Wilson
Angela Wilson
FridayJan 29 at 2:30pm

A normal distribution looks like a bell curve. The normal
distribution is used in statistics to describe how the variables
are distributed in the research study. This is especially
significant in evaluating whether the values from the variables
are reliable and significant to the research question.
Understanding the normal distribution can assist with the
evaluation of whether the study is worth translating into
practice. The bell curve can show the normal, medium, and
mean of the statistical data gathered from the study. Averages
can be calculated as well. The bell curve also shows a
relationship between the results of the interventions in a study.
For example, the tails of the distribution should be equal to the
peak in the distribution to determine that both variables are just
as likely as the next. This is extremely useful in deciding if the
results from the study meet the specifics of the practice setting
seeking evidence based practice improvements.
Angie
Kimberly Austin

Angie,
In your post, you mention "the bell curve also shows a
relationship between results of the interventions in a study". As
I picture this, what comes to mind is something very different
than what I used to picture when interpreting data. Since the
last time I took a statistics class, our world has changed; and
looking at a "curve" or the phrase, "flattening the curve" has
become much more well-known since the beginning of the
Covid-19 pandemic. Identifying the relationship between results
of the interventions has taken on great importance to more than
just scientists and scholars as the world tries to understand what
is happening with the spread of the Covid-19 virus. In an
interesting twist, seeing normal distribution exampled in
relationship to Covid-19 has helped me better understand a
concept I have struggled with in the past. I appreciate your post
in further clarifying the usefulness in determining how results
can be applied to practice improvements.
Kim
Collapse SubdiscussionKate Blue
Kate Blue
SaturdayJan 30 at 11:22am

Normal distribution is also called bell-shaped curve
distribution. The sampling distribution presents a wave curve
that is symmetrical. When sample sizes are large the data tends
to result in a normal distribution. When normal distribution of
the study sample is achieved, it will always be equal to the
general population. Looking at the standard deviations of the
data helps tell the accuracy of the sample. With a normal
distribution pattern, sixty-eight percent of the data is included
within one standard deviation to the left and right. Therefore,
the accuracy of data in a normal distribution is high (Polit,
2016). A DNP scholar would put a higher preference on a
study's data that resulted in normal distribution due to the
accuracy of sampling reflecting the normal population.
Reference
Polit, D. F. (2016). Nursing research: Generating and assessing
evidence for nursing practice (10th ed.). Wolters Kluwer
Health.

Kate Blue
Collapse SubdiscussionCarlos Legra Elias
Carlos Legra Elias
Hello Dr Kyzar and Class
Normal distribution can also be referred as a Gaussian
distribution which is a distribution of possibility that happens to
be symmetric in relation to the mean, which is an indication that
any data found close to the mean tend have an occurrence that is
more frequent in comparison to data that is not close to the
mean. When it is illustrated in a graph, it assumes the shape of
a bell. The distribution that is normal has various properties
that are interesting which include; has equal median as well as
mean, has a bell shape in addition to having a 68% of its data
falling within the deviation of 1 standard (Jabbari Nooghabi,
2020).
The deviation that is standard is the one in that is in control of
the distribution's spread. A standard deviation that is smaller is
an indication that the data is clustered closely around the given
mean, while normal distribution will end up being taller.
Standard deviation that is taller shows that the spreading of the

data is sparse near the mean hence the normal distribution tends
to be wider and flatter. Has at the center is basically symmetric,
50% of the values tend to be at the left side of the center and
50% of the values are situated at the right. The general region
under the cover basically is one and a normal model standard.
The importance of the normal distribution within statistics is
mainly because of its capability of being in a position to fit
many phenomena that are natural. For example, measurement
error, heights, scores related to IQ in addition to blood pressure
are done as per the distribution that is natural (Kim, et.al,
2020).
References
Jabbari Nooghabi, M. (2020). Process capability indices in
normal distribution with the presence of outliers. Journal of
Applied Statistics, 47(13-15), 2443-2478.
Kim, C., Cho, S., Sunwoo, M., Resende, P., Bradaï, B., & Jo, K.
(2020). A Geodetic Normal Distribution Map for Long-term
LiDAR Localization on Earth. IEEE Access.
Collapse SubdiscussionCarlene Yearwood

Carlene Yearwood
A normal distribution is a continuous probability distribution
that is symmetrical on both sides of the mean, so the right side
of the center is a mirror image of the left. The area under the
normal distribution curve represents probability and the total
area under the curve sums to one. Most of the continuous data
values in a normal distribution tend to cluster around the mean,
and the further a value is from the mean, the less likely it is to
occur. For a perfect normal distribution the mean, median and
mode are measures of central distribution of numerical values
important for any research findings. The normal distribution is
often called the bell curve because the graph of its probability
density looks like a bell.
The normal distribution is the most important probability
distribution in statistics because many continuous data in nature
and psychology displays this bell - shaped curve when compiled
and graphed for evidence. A normal distribution frequency
curve can be used to measure continuous variables, such as IQ,
height, weight and blood pressure. As a DNP scholar a normal
distribution is an important factor to consider towards the
contribution of accurate results for a practice change.

Reference
McLeod, S. A. (2019). Introduction to the normal distribution (
bell curve). Simply psychology:
https://www.simplypsychology.org/normal-distribution.html
Collapse SubdiscussionYosvani Garcia Boss
Yosvani Garcia Boss
SaturdayJan 30 at 9pm
Hello Dr. Kyzar,
Normal distribution which is also referred to as Gaussian
distribution, is basically a distribution of probability considered
to be symmetric in relation to the mean, which indicates that the
data that is close to the mean tend to be more frequent within
their occurrence as compared to the data that happens to be far
as from the mean. When in a graph form, the normal
distribution appears to be in the form of a bell curve. The
distribution is said to be an appropriate term in relation to a bell
curve of probability. Within a distribution that is normal, mean
happens to be zero while the standard deviation ends up being
one. This means its skew is zero while the kurtosis is three.
These types of distributions happen to be symmetrical though it

is not possible to say that every distribution that is symmetrical
is normal (Martínez-Flórez, et.al, 2020).
Distributions considered to be normal tend to have a regular
appearance in relation to statistics, in addition, they are known
to have some properties that are interesting which include
having the shape of a bell, has a median as well as a means that
are equivalent in addition to a data falls of 68% within a
standard deviation of 1. However, in reality, most distributions
for pricing can not be said to be normal perfectly. The model of
this distribution tends to be motivated by the theorem of Central
limit. It is a thesis stating that the calculated averages from the
variables that are independent as well as identically distributed
randomly tend to have distributions that are approximately
normal, despite of the form of distribution where the variables
get to be sampled from as long as it has a variance that is finite
(Mirfarah, et.al, 2021).
A times the normal distribution tends to be confused with the
distribution that is symmetrical. The symmetrical distribution
involves having the dividing line producing two images
mirroring each other, however, the real data might be in form of
humps that are two or a sequence of hills as well as the bell
curve indicating a distribution that is normal.

The main reason as to why normal distribution is of great
significance within statistics is due to the fact that it fits into
many phenomena that are natural. For instance, blood pressure,
heights, scores of IQ in addition to measurement error adhere to
the distribution that is normal. The distribution that is normal is
a function of probability used in describing the manner in which
a variable value gets to be distributed. It is known as a
distribution that is symmetric whereby most of the given
observation tends to cluster all over the peak that is central in
addition to the possibilities for values that are at a far distance
from the taper off of the mean equally within the two directions.
Values that are extreme within the two distribution tails tend to
be similarly not likely (Lee and McLachlan, 2020)
The statistical tests that are most powerful by the name
parametric which tend to be utilized by the psychologists need
the data to be distributed normally. In case the data is not in
resemblance with a curve in the bell shape, researchers might be
forced into using a type that is considered to be less powerful in
relation to the statistical test, which is referred to as statistics
that are non-parametric.
References

Lee, S. X., & McLachlan, G. J. (2020). On mean and/or
variance mixtures of normal distributions. arXiv preprint
arXiv:2005.06883. Retrieved from
https://arxiv.org/abs/2005.06883 (Links to an external site.)
Martínez-Flórez, G., Leiva, V., Gómez-Déniz, E., & Marchant,
C. (2020). A family of skew-normal distributions for modeling
proportions and rates with zeros/ones excess. Symmetry, 12(9),
1439. Retrieved from https://www.mdpi.com/2073-
8994/12/9/1439 (Links to an external site.)
Mirfarah, E., Naderi, M., & Chen, D. G. (2021). Mixture of
linear experts model for censored data: A novel approach with
scale-mixture of normal distributions. Computational Statistics
& Data Analysis, 107182. Retrieved from
https://www.sciencedirect.com/science/article/pii/S0167947321
000165 (Links to an external site.)

Theresa Kyzar
Hi Yosvani:
Thank you for your thoughtful response. Excellent! I really
appreciated the insight you provided about the main reasons that
the normal distribution is very significant in statistics and I
strongly agree with these points.
Please allow me to add to your comments.

With a normal distribution, you should expect to observe
approximately 68% of values within one standard deviation;
about 95% of the values within two standard deviations; and
about 99.7% are within three standard deviations. This is
referred to as the 68 95 99.7 rule or Empirical Rule (Statistics
How To, 2019). Exactly half the data is to the left as well as the
right side of the mean. To visualize this, with x being the
horizontal plane of the graph showing the data value scale and y
being the vertical plane showing the frequency scale (Vetter,
2017). An example could be a group of individuals who are
participating in a weight loss program. The x would be the
weights with the distribution ranging from 150 lbs. to 350 lbs.
The frequency would be how often each weight was recorded. In
a normal distribution, 250 lbs. would be the mean and median
with a bell shape. I think sometimes that students who are still
novices with these kind of experiences struggle with how to set
up their equations to properly understand the significance of the
process involved with normal distribution. Your own thoughts
about the importance of this and how non-parametric testing
comes into play add meaning to this discussion.
Kindly,

Dr. Kyzar
References
Statistics How To. (2019). What is the 68 95 99.7 rule?
Retrieved from
https://www.statisticshowto.datasciencecentral.com/68-95-99-7-
rule/ (Links to an external site.)
Vetter, T. R. (2017). Fundamentals of research data variables:
The devil is in the details. Anesthesia & Analgesia, 125(4),
1375-1380. http://dx.doi.org/10.1213/ANE0000000000002370
(Links to an external site.)
Kimberly Austin
Normal distribution, which is also known as Gaussian
distribution, or bell-shaped curve, is a symmetric, unimodal
distribution, and not too peaked probability distribution (Polit
& Beck, 2017).

Normal distribution is important in statistics as it is an essential
tool used for analysis and interpretation of data (Kumar &
Anila, 2017). Normal distribution fits many natural phenomena,
such as height, weight, blood pressures, etc. This probability
theory calculates corresponding values (Sokouti et al., 2017).
As data is plotted, important findings are revealed which
identify, standard deviation, mean, and mode.
References
Kumar, C. S., & Anila, G. V. (2017). On Some Aspects of a
Generalized Asymmetric Normal Distribution. Statistica, 77(3),
161-79.
http://dx.doi.org.chamberlainuniversity.idm.oclc.org/10.6092/is
sn.1973-2201/7134 (Links to an external site.)
Kluwer Health.
Sokouti, M., Sadehhi, R., Pashazedeh, S., Eslami, S., Abgadi,
H., Ghojazadeh, M., & Sokouti, B. (2017). Most accurate non-
linear approximation of standard normal distribution integral
based on artificial neural networks, Suranaree Journal of

Science & Technology, 24(3), 263-280.
https://chamberlainuniversity.idm.oclc.org/login?url=https://sea
rch.ebscohost.com/login.aspx?direct=true&db=a9h&AN=127020
973&site=eds-live&scope=site
Theresa Kyzar
Hi Angela, Kim, Carlos, Carlene, & Kate,
Thank you for your posts and for participating in this
conversation. You have all provided excellent responses which
expand upon the normal distribution!
Adding to your comments, I thought we could summarize the
features of a normal distribution which include:
symmetric bell shape is noted;
the mean and median are equal; both located at the center of the
distribution;
approximately equals, 68% of the data falls within one standard
deviation of the mean;
approximately equals, 95%of the data fall s within two standard

deviations of the mean; and
approximately equals, 99.7% of the data falls within three
standard deviations of the mean.
normal distribution.svg
Take care,
Dr. Kyzar
References
Khan Academy. (2010, January 8). Normal distribution
problems: Empirical rule [Video]. YouTube.
https://youtu.be/OhRr26AfFBU (Links to an external site.)
Khan, S. (2020). Normal distribution problems: Empirical rule.
Retrieved from https://www.khanacademy.org/math/ap-
statistics/density-curves-normal-distribution-ap/stats-normal-
distributions/v/ck12-org-normal-distribution-problems-
empirical-rule (Links to an external site.)
Edited by Theresa Kyzar on Jan 31 at 6:18pm

Racheal Aneke
What is a normal distribution?
The most important probability distribution in statistics (Links
to an external site.) is normal distribution because it fits many
natural occurrences and curves for a real-world random
variable. It describes how the values of variables are
distributed evenly. The normal distribution also functions for
independent randomly generated variables. The readings are
assembled at the top, and the probability values from the mean
(Links to an external site.) are narrow or equal on both sides.
Heights, weight, blood pressure, and IQ scores are examples of
normal distribution (Gray. J., & Grove. S. K., 2021). Normal
distribution is also called bell curve or the Gaussian
distribution. The common thing about normal distribution is that
mean, median (Links to an external site.), and mode (Links to
an external site.) are all equal. The Empirical Rule or the 95%
rule allows you to determine the proportion of values that fall
within certain distances from the mean.
What does it look like?
Week 4 picture.png

This is a normal distribution with some interesting properties
such as the bell shape or curve, the mean and median are equal,
and 68% of the data are within a standard deviation (Gray. J., &
Grove. S. K., 2021).
Why is the normal distribution important in statistics?
A hypothesis test assumes that figures follow a regular
circulation in a normal distribution since the mean, median, and
mode are equal. A normal distribution is essential in statistics
because its probability distribution does not fit all populations
to create variety. Linear and nonlinear degeneration (Links to
an external site.) both assume that the residuals (Links to an
external site.) also follow a normal distribution (Gray. J., &
Grove. S. K., 2021). The central limit theorem (Links to an
external site.) in a probability theory established that, in some
situations, if independent variables are added, the sum
normalizes the curve even if the original variables were not
distributed normally.
Reference:
(Gray. J., & Grove. S. K., 2021) Burns and Grove's the practice

of nursing research: appraisal, synthesis, and generation of
evidence. Elsevier.
Collapse SubdiscussionEjiro Ohonbamu
Ejiro Ohonbamu
Good evening Dr. Kyzar and classmates.
Appraising Descriptive Statistics
rationale.
Quantitative research studies use a power analysis to
approximate an actual sample size needed for a practical
deduction. However, large sample sizes are desirable to small
samples. The reason for this is that the large samples boost
statistical conclusion legitimacy, which tends to be more
typical. The researchers performed a power analysis at the early
stage of the study to estimate the sample size and to assist in
the reduction of possible errors of Type II (Polit & Beck, 2018).
Basically, sample size defines the number of participants
needed in a study. If the research calls for a specific sample to

determine a specific outcome, it is essential that that is
followed. On that note, with every large or small sample, there
is a probability of errors if the sampling technique is not
followed. The standard deviation promotes caring for the
certainty that the sample is not typically dispersed.
chi-square analysis to analyze nominal and ordinal data. Is this
the appropriate level of statistical analysis to answer the
The importance of the Chi-square value is noted by using the
appropriate degree of freedom and significance (Turhan, 2020).
Usually, the Chi-square determines the relationship between the
two existing variables in a sample setting and the probability to
show an actual relationship amongst the two variables in a
given situation. It also assesses some uncompromising variables
are connected with some populations, because variables tend to
be a bit diverse from their populations (Turhan, 2020). In data
analysis, it is mandatory to find degrees of freedom, expected
occurrences, test statistics, and P-value connected to the test
statistic (Turhan, 2020).

The researcher reports that the p-level led her to conclude that
the null hypothesis was rejected. In your critique of the study,
you determine that the null hypothesis is correct. Do these
findings impact your decision about whether to use this
evidence to inform practice change? Why or why not?
It is essential to note that the value P is the observation
possibility of a sample statistic that is the highest test of
statistics (Turhan, 2020). Now each hypothesis test uses the p-
value to assess the depth of evidence. It shows that the null
hypothesis untrue, which establishes that this could be a
mistake. The null hypothesis is truthfully based on statistical
errors and might institute further need added investigation. On
that note, Type I error occurs when the null hypothesis is
rejected when it is true. There is a greater risk of a Type I error
with a 0.05 level of importance as compared to a 0.01 level of
significance. The higher the level of significance, the more
extreme, nature of the risk of a Type I error diminishes (Polit &
Beck, 2017).
The null hypothesis is not obviously voiced in many research
studies. In this case, it is, and it will impact my decision
because an error has been determined by the researcher. In

particular, scientists are challenged and not convinced with
time‐ to‐ event results intensely understanding the null
hypotheses based on hazard roles and their uses (Stensrud et al.,
2019).
References
Stensrud, M., J., Roysland, K., & Ryalen, P., C. (2019). On null
hypotheses in survival analysis.
Biometrics. 75(4),1276-1287. http://doi:10.1111/biom.13102.
Turhan, S., N. (2020). Karl Pearson's Chi-Square Tests.
Educational Research and Reviews,16
(9), 575-580.

https://eric.ed.gov/contentdelivery/ser vlet/ERICServlet?accno=
EJ1267545
Theresa Kyzar
Hi Ejiro:
Good response this week! You offered some very interesting
perspectives, and I would like to take this brief oppor tunity to
add to your discussion. My comments relate specifically to the
use of the Chi Square Analysis. This type of test along with
correlations, t-tests, and ANOVA, are foundational techniques,
typically covered in introductory statistics textbooks and
introductory statistics classes. Chi-square tests are by far the
most popular of the non-parametric or distribution free tests and
the default choice when applied psychological researchers
analyze categorical data (Sharpe, 2015). It is important to note
that it is an important, useful when both variables are nominal -
level data, e.g., simple counts of people within a category such
as gender or ethnic group membership). Tappen (2016)
discusses that the Chi-square test compares the observed
(actual) frequencies in each cell to the expected frequencies (p.
350). However, remember that a key objection is that when the

total number of observations is under thirty or when one or
more expected cell frequencies are under five for total numbers
under fifty then Fisher's exact test should be used in place of
the Chi-square test (Langley, 1970; Siegel, 1956; Zar, 1974)[as
cited by Hoffman, 1975]. Failure to do so may lead to an
erroneous probability of rejecting the null hypothesis.
Kindly,
Dr. Kyzar
References
Hoffman, J.E. (1975). The incorrect use of Chi-square analysis
for paired data. Clinical and Experimental Immunology, 24,
227-229.
Sharpe, D. (2015). Chi-square test is statistically significant:
Now what? Practical Assessment, Research, and Evaluation,
20(8). doi: https://doi.org/10.7275/tbfa-x148 (Links to an
external site.). Retrieved
https://scholarworks.umass.edu/pare/vol20/iss1/8 (Links to an
external site.).

Tappen, R. (2016). Advanced nursing research: From theory to
practice (2nd ed). Jones & Bartlett.
Theresa Kyzar
Important PLEASE READ: Week 4 Content Summary
Hi Class:
As we approach the weekend, here are tips from Week 4 and
Week 5. These tips will assist you as you prepare for the week 5
quiz!
Week 4 Descriptive Statistics Key Points & Questions
Properties of Descriptive Statistics (Polit & Beck, 2017)
Key Points-Descriptive Statistics
Summarize or describe characteristics of a data set.
They are numerical and graphical summaries of data.
Consist of two basic categories of measures: measures of central
tendency and measures of variability or spread.

Measures of central tendency describe the center of a data set.
Measures of variability or spread describe the dispersion of data
within the set.
Help decide how large of a sample is needed to make sure
subjects.
Must be used to determine sample size before the study begins.
Chi-Square Analysis (Ali & Bhaskar, 2016; Connelly, 2019;
Polit & Beck, 2017)
Is a type of inferential statistic.
o Evaluates if two categorical variables (e.g., gender,
educational level, race, etc.) are related (correlated) in any way.
o Appropriate for discrete variables (nominal, ordinal).
o Does not work with continuous variables (interval, ratio)
Null Hypothesis Testing (Ali & Bhaskar, 2016; Polit & Beck,
2017)

o The beginning point for statistical significance testing.
o A formal approach to deciding between two interpretations of
a statistical relationship in a sample
o Null Hypothesis – suggests there is no relationship between
variables, populations, etc. meaning there was an error in
sampling.
o Alternative Hypothesis – suggests there is a relationship
between variables, populations, etc.
o Rejecting the Null Hypothesis – suggests being in support of
the Alternative Hypothesis.
Inferential Statistics (Ali & Bhaskar, 2016)
Allow one to make inferences (conclusions, predictions,
associations, correlations) from data.
Take data from samples and make generalizations about a
population.
Example: Say you are interested in an intervention to improve

care at life’s end in the inpatient setting. Using inferential
statistics, you take the data from your sample and assess
whether or not the data can be expected to demonstrate that the
intervention will work at the population level.
P-value (Taylor, 2019)
With most analyses, an alpha of 0.05 is used as the cutoff for
statistical significance.
o If the p-value is less than 0.05, we reject the null hypothesis
that there's no difference between the means and conclude that a
significant difference does exist
o If the p-value is larger than 0.05, we cannot conclude that a
significant difference exists.
o The p-value is also known as the calculated probability.
o The p-value is commonly referred to as the alpha.
Questions for your review as you prepare for the Week 5 Quiz.
What are inferential statistics used for?
Inferential statistics are used to make inferences (conclusions,

predictions, associations, correlations) from a sample to a larger
population (Ali & Bhaskar, 2016).
What is it called when research findings and conclusions from a
study conducted on a sample population are used with the
population at large?
Generalizability is the term used to describe when research
findings and conclusions from a study conducted on a sample
population are used with the population at large (Ali & Bhaskar,
206; Polit & Beck, 2017).
What are some examples of nominal and ordinal variables?
Interval – weight, height
Ratio – Level of pain: 0 to 10
Nominal – gender, blood type, marital status, hair color
Ordinal – socioeconomic status (e.g., low, middle, high), course
grades (e.g., A, B, C), high school class ranking (e.g., 1st, 9th,
45th...)
What is a normal distribution and what does it look like?
Represents the distribution of many random variables as a
symmetrical bell-shaped graph.
With a normal distribution, you should expect to observe

approximately 68% of values within one standard deviation;
about 95% of the values within two standard deviations; and
about 99.7% are within three standard deviations. This is
referred to as the 68 95 99.7 rule or Empirical Rule (Statistics
How To, 2019).
What are some of the properties of descriptive statistics?
Summarize or describe characteristics of a data set.
They are numerical and graphical summaries of data.
Consist of two basic categories of measures: measures of central
tendency and measures of variability or spread.
Measures of central tendency describe the center of a data set.
Measures of variability or spread describe the dispersion of data
within the set.
What is the most precise definition of a sample?
The sample is a subset of the larger population that a researcher
aims to study.
What is the difference between the p-value and alpha?
When performing hypothesis testing in statistics, a p-value
helps to decide the significance of results. A p-value is a
number between 0 and 1.
A small p-value (typically ≤ 0.05) indicates strong evidence
against the null hypothesis, so the null hypothesis is rejected

(Taylor, 2019).
The alpha level is the probability of rejecting the null
hypothesis when the null hypothesis is true or the probability of
making a wrong decision (Taylor, 2019).
What is the name of the hypothesis that is accepted when
significant differences exist?
Alternative hypothesis
What is random sampling and why is it important?
Random sampling is choosing small groups (subsets) of a
population with each person having an equal chance of being
chosen. Random sampling is important in that is supposed to be
representative of the larger population (Polit & Beck, 2017).
Random sampling controls sampling bias also called sampling
selection bias (Krautenbacher, Theis, & Fuchs, 2017).

1 2 6Strategic Communications PlanAysel Kara

Recommended

Recommended

More Related Content

Similar to 1 2 6Strategic Communications PlanAysel Kara

Similar to 1 2 6Strategic Communications PlanAysel Kara (20)

More from MartineMccracken314

More from MartineMccracken314 (20)

Recently uploaded

Recently uploaded (20)

1 2 6Strategic Communications PlanAysel Kara