Discuss the Meaning of Statistical Significance
This assignment has two parts
Assignment: Part 1
Discuss the Meaning of Statistical Significance
Define statistical significance and also answer questions about
concepts that were unclear in the lessons.
To receive full credit for this assignment, post the following:
– Make a post that includes a short paragraph (three to four
sentences), and explain in your own words what is meant by statistical
– On the same post as the paragraph, add a single sentence that describes an idea or concept in quantitative data analysis methods that you did not fully understand or for which you would like further explanation.
Assignment: Part 2
Identify the Data Analysis of a Study
For this assignment, you will evaluate the data analysis of the quantitative study below (I will upload the article for you). Read through the article:
As you go through the article, use the following worksheet/study guide (I will upload for you). Answer the questions to make notes about the questions that will be asked on the test. You will be asked several multiple choice and several short-answer questions about the research methods used in this study. You can reference the notes you took while reviewing the articles during the test.
– Read through the article to become familiar with the content.
– Answer the questions on the worksheet/study guide to make notes
about the questions that will be asked on the test.
Below is an Overview of the lesson. Also, the lessons for this
assignment as well as expected outcomes after reviewing them. I will
upload the slides and transcripts of the lessons for you to look at,
review and use.
You will learn about quantitative data analysis methods and some of
the underlying assumptions of these methods. Because data analysis
methods are unique to both quantitative and qualitative research, we
will focus on only quantitative analysis methods in this module and
then focus on qualitative analysis methods in another module. The
level of measurement is central to how we analyze quantitative
methods, so you may want to briefly review the four levels of
measurement (nominal, ordinal, interval, ratio) from the previous
Why is it important to understand quantitative data analysis methods?
Although there are many answers to this question, in the context of
this course it is important to understand to evaluate research
findings and the implication of the findings in your evidence-based
practice. An inadequate understanding of quantitative analysis methods
(the same applies to qualitative analysis methods) can result in
altering your practice based on misunderstood evidence.
Finally, some students find learning about quantitative data analysis
methods intimidating. For this course, you are not expected to know
how to conduct analyses, rather, the aim is to provide you with
foundational knowledge and concepts so you can more critically
evaluate quantitative research.
By the end of this module, you should be able to do the following:
– Identify the different measures of central tendency and variation.
– Summarize the relation between sample data distributions and data
distributions in the population (i.e., “normal curve”).
– Identify the data distribution assumptions of a particular
inferential statistical test.
– Evaluate the evidence presented in a published article for the
distributional assumptions associated with the utilized inferential
Visualizing Data –
Because most quantitative studies collects lots of individual data, it
is useful to display these data statistically using a visual format
that aggregates findings. This presentation introduces several methods
to visualize these data, including tables, histograms, and bar charts.
As you look at these data visualizations, you’ll begin to see examples
of the normal distribution curve, which is an important concept that
will be covered in more detail. (I will upload the presentation slides
and transcript for you to go over).
Measures of Central Tendency and
In addition to data visualizations, researchers can better communicate
their findings by using calculations that indicate a typical score.
This lesson explains how to calculate three types of basic statistical
indicators, mean, median, and mode, and how each uses a different
method to identify the “average” score in a study. You’ll learn both
the benefits and limits of each indicator, including recommendations
on when each one should be used. The lesson also covers standard
deviation, which is a more complex calculation that reveals how much
individual data vary along a normal curve. (I will upload the
presentation slides and transcript for you to go over).
Inferential Statistics –
CENTRAL LIMIT THEOREM –
Thus far, this module has covered statistics that describe or
summarize the data collected in a study. Researchers are also
interested in making probability statements about a target population,
and they use inferential statistics to accomplish this goal. One such
statistic is the Central Limit Theorem, which is covered in this
lesson. You will learn how researchers can make inferences from a
standard deviation of a population by using the means of repeated
random samples. (I will upload the presentation slides and transcript
for you to go over).
STATISTICAL SIGNIFICANCE –
When making probability statements, it is important to note the
likelihood that the findings of a sample occurred by chance and do not
accurately reflect the population measured. This lesson covers the
concept of statistical significance, which helps identify data that
are highly likely to occur if the study is repeated multiple times.
You’ll learn about what is considered an acceptable level of risk when
making probability statements, and also how that is expressed within a
study. (I will upload the presentation slides and transcript for you
to go over).
Types of Error –
There are two categories of errors that can be made when using
inferential statistics: false positives and false negatives. This
lesson explains both errors and how they are categorized in a study.
It also introduces some of the common statistical procedures used to
reduce the likelihood of errors. These procedures will be covered in
greater detail in future modules, but make note in this lesson of what
types of studies would use each procedure.
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