## Discuss the Meaning of Statistical Significance

Paper Requirements:

This assignment has two parts

Assignment: Part 1

Discuss the Meaning of Statistical Significance

Instructions:

Define statistical significance and also answer questions about

concepts that were unclear in the lessons.

Guidelines:

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

significance.

– 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

Instructions:

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.

Guidelines:

– 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.

Overview:

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

module.

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.

Expected Outcomes:

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

test.

Lessons:

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

Variability –

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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