Assignment 4: Analyzing Data In Research

Did you find the flow chart above to be important? Do you think it (or a similar one) would assist you with your future projects?

Give some thought too about how you will deal with false-positive results when you are analyzing your own data that will be collected during the implementation of your upcoming DNP Project. What is the importance of true statistical data interpretation in published research studies?

Instructions:

Use an APA 7 style and a minimum of 200 words. Provide support from a minimum of at least (1) scholarly sources. The scholarly source needs to be: 1) evidence-based, 2) scholarly in nature, 3) Sources should be no more than five years old (published within the last 5 years), and 4) an in-text citation. citations and references are included when information is summarized/synthesized and/or direct quotes are used, in which APA style standards apply.

• Textbooks are not considered scholarly sources.

• Wikipedia, Wikis, .com website or blogs should not be used.

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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. Assignment 4: Analyzing Data In Research

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. Assignment 4: Analyzing Data In Research