Scenario Statistical significance is found in a study, but the effect in reality is very small (i.e., there was a very minor difference in attitude between men and women). Were the results meaningful? An independent samples t test was conducted to determine whether differences exist between men and women on cultural competency scores. The samples consisted of 663 women and 650 men taken from a convenience sample of public, private, and non-profit organizations. Each participant was administered an instrument that measured his or her current levels of cultural competency. The cultural competency score ranges from 0 to 10, with higher scores indicating higher levels of cultural competency. The descriptive statistics indicate women have higher levels of cultural competency ( M = 9.2, SD = 3.2) than men ( M = 8.9, SD = 2.1). The results were significant t (1311) = 2.0, p <.05, indicating that women are more culturally competent than are men. These results tell us that gender-specific interventions targeted toward men may assist in bolstering cultural competency. Instructions: Critically evaluate the scenario you selected based upon the following points:     Critically evaluate the sample size.     Critically evaluate the statements for meaningfulness.     Critically evaluate the statements for statistical significance.     Based on your evaluation, provide an explanation of the implications for social change.  I attached the scenario to this message add at least two references and citations Number of Pages: 1 Page Statistical significance is found in a study, but the effect in reality is very small (i.e., there was a very minor difference in attitude between men and women). Were the results meaningful? According to The American Statistical Association's Statement on the Use of P Values (2016), Statistical reasoning should not be replaced by P-values. Once significance is noted and the effect is very small, the researcher should not conclude significance just because the P-value suggested so. Other tests should be performed which include construction of confidence intervals. If after several tests significance is still being seen, then the researcher can conclude significance otherwise, he/she should opt for other approaches for the research such as increasing the sample size. Sample Sizes Sample sizes used were 663 for women and 650 for men. The data was collected from three points that is public, private and non-profit organizations. Though the sample seems good enough, the entire population figure is not provided. Another problem is that, sample sizes from the three locations were not disclosed which probably means that the samples were all added up and used in the analysis. Sample sizes from each location should be carefully determined based on the population of each location. Since Convenience sampling is error bound, the samples shouldn’t be summed up and used in the analysis. Each location should be examined separately. Thi.

1. Scenario
Statistical significance is found in a study, but the effect in
reality is very small (i.e., there was a very minor difference in
attitude between men and women). Were the results meaningful?
An independent samples t test was conducted to determine
whether differences exist between men and women on cultural
competency scores. The samples consisted of 663 women and
650 men taken from a convenience sample of public, private,
and non-profit organizations. Each participant was administered
an instrument that measured his or her current levels of cultural
competency. The cultural competency score ranges from 0 to
10, with higher scores indicating higher levels of cultural
competency. The descriptive statistics indicate women have
higher levels of cultural competency ( M = 9.2, SD = 3.2) than
men ( M = 8.9, SD = 2.1). The results were significant t (1311)
= 2.0, p <.05, indicating that women are more culturally
competent than are men. These results tell us that gender-
specific interventions targeted toward men may assist in
bolstering cultural competency.
Instructions: Critically evaluate the scenario you selected based
upon the following points:
Critically evaluate the sample size.
Critically evaluate the statements for meaningfulness.
Critically evaluate the statements for statistical significance.
Based on your evaluation, provide an explanation of the
implications for social change.
I attached the scenario to this message
add at least two references and citations

2. Number of Pages: 1 Page
Statistical significance is found in a study, but the effect in
reality is very small (i.e., there was a very minor difference in
attitude between men and women). Were the results meaningful?
According to The American Statistical Association's Statement
on the Use of P Values (2016), Statistical reasoning should not
be replaced by P-values. Once significance is noted and the
effect is very small, the researcher should not conclude
significance just because the P-value suggested so. Other tests
should be performed which include construction of confidence
intervals. If after several tests significance is still being seen,
then the researcher can conclude significance otherwise, he/she
should opt for other approaches for the research such as
increasing the sample size.
Sample Sizes
Sample sizes used were 663 for women and 650 for men. The
data was collected from three points that is public, private and
non-profit organizations. Though the sample seems good
enough, the entire population figure is not provided. Another
problem is that, sample sizes from the three locations were not
disclosed which probably means that the samples were all added
up and used in the analysis. Sample sizes from each location
should be carefully determined based on the population of each
location. Since Convenience sampling is error bound, the
samples shouldn’t be summed up and used in the analysis. Each
location should be examined separately. This is to allow for
examination of the error and comparison of results. What the

3. researchers have done in this study regarding the samples and
their sizes is not statistically sound and that will affect the
findings.
Meaningfulness and Statistical Significance Combined
Convenience sampling is a statistical mode of obtaining
representative samples by selecting the samples because of the
ease of access. For this reason, convenient samples are mostly
biased and bound to have errors with high probabilities. Thus,
we cannot generalize our findings to the entire population and
also make inference about the entire population. Since the
sample is not representative of the population, the results of the
study cannot speak for the entire population. If this is done, it
will result in a low external validity of the study.
In this scenario, results from a convenience sample have been
generalized and used to make conclusions about the entire
population. This is a statistical effect.
“The results were significant t (1311) = 2.0, p <.05,
indicating……………………….”
When we give significance, the calculated P-value must be
provided. In this scenario an indefinite P-value of <0.05 has
been quoted. This leads the interested parties in the dark since
they do not know if the P-value was 0.049 or 0.01 and thus
aren’t able to judge how strong or weak the significance was.
This is a grave error in statistics
Social change
Correct use of research tools is relevant for any statistical
research and the society in general. Misuse/ignorance in use of
statistical tools and methods will not only lead to wrong
findings, but also put the society at risk of a scientific crisis.
Scenario

4. The p-value was slightly above conventional threshold, but was
described as “rapidly approaching significance” (i.e., p =.06).
An independent samples t test was used to determine whether
student satisfaction levels in a quantitative reasoning course
differed between the traditional classroom and on-line
environments. The samples consisted of students in four face-
to-face classes at a traditional state university (n = 65) and four
online classes offered at the same university (n = 69). Students
reported their level of satisfaction on a fivepoint scale, with
higher values indicating higher levels of satisfaction. Since the
study was exploratory in nature, levels of significance were
relaxed to the .10 level. The test was significant t(132) = 1.8, p
= .074, wherein students in the face-to-face class reported lower
levels of satisfaction (M = 3.39, SD = 1.8) than did those in the
online sections (M = 3.89, SD = 1.4). We therefore conclude
that on average, students in online quantitative reasoning
classes have higher levels of satisfaction. The results of this
study are significant because they provide educators with
evidence of what medium works better in producing
quantitatively knowledgeable practitioners
Instructions: Critically evaluate the scenario you selected based
upon the following points:
Critically evaluate the sample size.
Critically evaluate the statements for meaningfulness.
Critically evaluate the statements for statistical significance.
Based on your evaluation, provide an explanation of the
implications for social change.

5. a) Sample size:
As said by Mugenda (1999), a sample size of five percent of the
whole population is already enough for the analysis. In this
case, the population of the school is not given. However, it is
reasonable to presuppose that 132 represents more than or equal
to 5% population. If the study is hypothesis-driven, the sample
size (n=65 and n=69) in both control and experimental setups,
respectively, was too big for the statistical test used that was
the t-test. Since the sample size is larger than 30, Z-test may be
used instead of t-test, if normal distribution is applied.
Statements for meaningfulness:
Instead of mentioning “rapidly approaching significance”, it is
more appropriate to say that the P-value of 0.06 is on the edge
of significance. The former statement suggests that the P-value
is reducing and to make it even worse-sounding, the word
“rapidly” has been used, which is not actually statistically
correct.
Statistical significance:
Using t-test must determine if there was a significant difference
between the traditional classroom (control group) and on-line
environments (experimental group). However, it was noted that
the study is exploratory and hypothesis testing is not necessary.
Hypotheses statements are not used since the research is not
descriptive. With absence of hypotheses statements, p-values
were not really meaningful and only served as a rough guide. P-
values and their critical counterparts are used in descriptive and
correlation forms of research. While it is all right to conduct
exploratory analyses on sample plots positioned in accordance
with a thorough, purposive sampling design, such meticulous
placement is not compulsory. In exploratory research, the
researcher is only interested in providing details where very
little is known about the phenomenon. It may use various

6. procedures such as trial studies, interviews, group discussions,
experiments or other tactics for the purpose of gaining
information. Therefore the statement, “Since the study was
exploratory in nature, levels of significance were relaxed to the
.10 level”, is not statistically sound.
The statement, “The test was significant t (132) = 1.8, p = .074,
wherein students in the face-to-face class reported lower levels
of satisfaction (M = 3.39, SD = 1.8) than did those in the online
sections (M = 3.89, SD = 1.4)”, is not just statistically
incorrect, but also confusing and misleading. T-test would not
be applicable here as we have two groups which are further
subdivided into 4 subgroups each. Samples are to be taken from
each sub group and an Analysis of Variance (ANOVA) may be
performed. Furthermore, traditional threshold of 0.05 is always
recommended for use.
Implications for social change:
The first scenario showed great depth of confirmation of how p-
values are generally misunderstood and misused. With more
stress and importance on the subject matter, these errors can be
avoided in future. Positive social changes will have a great
impact in appreciating and understanding the true issue.
The result of the study is important in determining how the
investigator can optimally explain or describe the variation in
the data set through data-diving. The investigator may find
patterns in the students’ answers that may propose evidences to
educators on what medium works more effectively in the
classroom.
b)
Sample Size:
Sampling, in this scenario, confirmed the results but seemed to
be so small although it represented the public, private and non-
profit sectors. In collecting quantitative data, a statistical
formula must be used to give a rough estimate of the sample
size that is needed. There are always errors in sampling that
reflect the differences arising between statistics and the
population, if the sample is not an accurate representative of the

7. entire population from which it was taken from. By increasing
the sample size, the error can be minimized. It can even be
eliminated by interviewing the whole population.
It can therefore be agreed that even though representative of the
particular sectors of the employees, a sample size of n=432 is
quite small. The findings may remain valid and relevant as the
level income is always perceived to increase job satisfaction.
This is because a large sample is recommended if there is high
variability and small sample if variability is minimal.
Statements for meaningfulness:
The findings indicated that as the level of income increased, the
job satisfaction increased as well. The findings reflected a
reality in practical terms. This can be the reason why people
fight for promotions, as the higher you move in the organization
hierarchy, the greater the income, resulting to greater
satisfaction. Also, people compete for jobs in high paying
companies than companies known to be low at remunerating. To
a great deal, this is meaningful and reflects the societal
inclination towards money.
Statistical significance:
The result of the test demonstrated a strong positive correlation
between the two variables, r =.87, p < .01, showing that as level
of income increases, job satisfaction increased as well. The
findings of this result were thus statistically significant because
it is confirmed by the strong correlation of .87 or 87% and
further by the statistical significance of .01 almost closing to
.00 which indicates perfect linear relationship. The null
hypothesis is, thus, rejected.
This implies that it is very sure that the relationship is real, and
thus, the conclusions that the levels of satisfaction among
employees are determined by the level of income. There lies a
greater validity as the levels of significance remained at .05 or
5%. Thus, the researcher is 95% confident that the increase in
the level of income increases job satisfaction.
Implications for social change:
The findings indicated that as the level of income increased, the

8. job satisfaction increased as well, and this may be against the
socialist-economist people who have put forward a strong
argument that other factors such as the nature of job, work
environment, management and the hierarchical structures, apart
from money, play a key role to job satisfaction. Most of the
economists believe that favorable work environments, flat
organizations, less bureaucratic and co-operative management
are essential in determining employee satisfaction.
Nevertheless, money is still of primary importance to most
people, and it can be concurred that the level of income is the
determinant of job satisfaction as per the findings .Thus,
definitely, as life becomes harder, a means of living (money) is
always what the employees are chasing.