Expert answer:Response to Discussion regarding Type 1 and Type I
Solved by verified expert:Please provide a brief response discussion and provide reference within 5 years. I will attach the file discussion numbered 1 to 4.
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Number 1
Type I is when the researcher claims the level of efficacy was met when
actually it wasn’t. This is the alpha probability of flaws/errors in statistics,
according to Houser (2018). This is a serious error in which the researcher
claims results to be effective without mitigating the probability of uncontrolled
variables. Potentially, this type of error could be lethal if the research directly
impacts the person such as in drug therapy. The higher the probability of error
the less valid the study. According to Houser, the acceptable alpha rate is a
level of significance less than 0.05.
Type II error is the beta or statistical testing power in statistics (Houser, 2018).
This is when the researcher reports there were no impacts or outcomes of the
treatment or problem statement when actually there were. One of the ways to
mitigate this error is to insure a large enough sample size. The researcher does
not acknowledge there were findings to the intervention in the study.
To maintain the integrity of internal validity is to have a solid design study. The
researcher should be proactive in mitigating any potential barriers or outcomes
by identifying a plan to mitigate their influences. One way doing this is to have
solid selection of the participants. The researcher could inject bias into the
study by means of the selection process. An example of this would be when
picking participants to fit into a too narrowed of a testing group or too broad.
Professionally, I encountered this issue recently regarding a Hep C medication
with one of my patients. He is a TBI patient and the study on this particular
drug does not include TBI population, yet the drug is still being used. Could this
be a question in the internal validity regarding treatment of the published
data? From the nursing perspective I would question the validity of the drug
and its published side effects.
Overall, there are other factors in maintain internal validity such as consents,
attrition, instruments used, and historical effects are some factors to consider.
Houser, J. (2018). The importance of research as evidence in nursing. In
Nursing research reading, using, and creating evidence (4th ed.). Burlington,
MA: Jones and Bartlett Learning.
Grand Canyon University. (2017, October 26). NUR 504: Week 6 | Descriptive,
Inferential, and Multivariate Statistics [on line]. Retrieved from https://lcgrad3.gcu.edu/learningPlatform
Number 2
Type I and type II errors are errors that involve the null hypothesis. A null
hypothesis is “a hypothesis stating no relationship between the variables under
study; used primarily in statistical testing as the hypothesis to be rejected”
(Polit & Beck, 2017). Type I error is when researchers discover a false positive
or they have rejected the null hypothesis even though it is actually true. A type
II error is when researchers do not reject the null hypothesis even though the
alternate hypothesis is the correct one. This is known as a false negative. When
errors occur, they cause the research and results to be misleading.
The researcher is responsible for looking at all the data and establishing the
risk of error. If there is a type I error, then looking at medications affecting a
disease, the researcher would say the drug helps with the disease. In reality,
the drug does not kill the disease at all. In a type II error, it would be opposite
of a type I error. The researchers would conclude that the drug had no affect
on the disease when it actually would kill it.
The researcher must test their data throughly and validate their results.
Researchers must accept the results of their studies, instead of working to try
and validate their hypothesis. When they validate their statistical data, they
are making sure there is the least amount of errors possible.
Reference:
Polit, D. F., & Beck, C. T. (2017). Nursing research: Generating and assessing
evidence for nursing practice (10th ed.). Philadelphia, PA: Lippincott Williams &
Wilkins.
Number 3
Type I errors are equivalent to false positives. It encompasses the denunciation
of a null hypothesis that is actually true. It occurs when we are observing a
difference when in truth there is none. Meaning there is no statistically
significant difference.
A type II errors are equivalent to false negative. It is a statistical term used
within the framework of hypothesis testing that describes the error that occurs
when one accepts a null hypothesis that is actually false. This is the error of
failing to accept an alternative hypothesis when you don’t have adequate
power. Meaning, it occurs when we are failing to observe a difference when in
truth there is one. The error rejects the alternative hypothesis, although it
does not occur due to chance. A type II error fails to remove, or accepts, the
null hypothesis, although the alternative hypothesis is the true state of nature.
The chance of getting type II error can be decrease, by increasing the sample
size (Polit & Beck 2017).
Type I and type II errors are part of the process of hypothesis testing. Although
the errors cannot be eliminated, we can minimize one type of error. When
conducting a hypothesis test, the probability, or risks, of making a type I error
or type II error should be considered.
Name one thing that can be done to improve internal validity of a study.
Internal validity refers to how well an experiment is done, especially whether
it avoids more than one possible cause acting at the same time. The less
chance for this to happen in a study, the higher its internal validity is. Internal
validity can be improved in a few simple ways. One way is for investigators to
use single and double-blind techniques. A single-blind study is where the
participant does not know the condition they are in, and double-blind is where
neither the participant nor the experimenter knows what the groups
represent. This method ensures there is little demand characteristics, such as
trying to behave a certain way because they think that is what is expected of
them, and decreases researchers’ effects, as they cannot even accidently have
a bias to a certain group.
Polit, D., & Beck, C. (2017). Nursing research. Generating and assessing
evidence for nursing practice (10th ed.). [Grand Canyon University Digital
Resources]. Retrieved from:
https://viewer.gcu.edu/E7ePxX
Number 4
A Type I error is when you reject the null hypothesis even if it is true (false
positive) and a Type II error is when the null hypothesis is not true and is
accepted (false negative/ failing to reject).
Example:
Type I error: When a person does not have breast cancer, yet the
mammogram says she does.
Type II error: When a person does have breast cancer, yet the mammogram
fails to pick it up.
These errors are important because sometimes, by chance alone, a sample is
not representative of the population. Then the results of the sample does not
reflect the reality in the population, and the random error leads to an
erroneous inference (Banerjee, Chitnis, Jadhay, Bhawalkar & Chaundhury,
2009).
Internal validity refers to the extent to which it is possible to make an
inference that the independent variable, rather than another factor, is truly
causing variation in the dependent variable (Polit & Beck, 2015). Having a
strong framework and being open about all the biases that can arise is an
important factor in improving internal validity. Basically, “covering all your
bases” and having a “plan B”. Often, researchers identify the research
problem and then go in search of a theory. I think if they also studied the
cause and then the effect, they would see all possible tangents occurring from
the research.
Reference
Banerjee, A., Chitnis, U., Jadhay, S., Bhawalkar, J., and Chaundhury, S.
(2009). Hypothesis testing, type 1 and type 11 errors. Industrial Psychiatry
Journal. 18(2), 127-131. Retrieved from:
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2996198/
Polit, D & Beck, C. (2012). Nursing research: Generating and assessing
evidence for nursing
practice. Philadelphia, PA: Lippincott Willians & Wilkins
…
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