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2023 Min 300 words 1 scholar article no older than 5 years APA format Post
by adminNursing 2023 Post – Need done in 12 hours!
Min 300 words 1 scholar article no older than 5 years APA format Post 2023 Assignment
Min 300 words, 1 scholar article (no older than 5 years), APA format
Post
……………………………………..
Reflect on the concepts and practices you have learned on healthcare systems, politics, and health policy. Read the article that is linked below and share insights as how to make informed decisions on nursing practice and patient outcomes on a global basis.
In addition, state how you will apply what you have learned in this course to your upcoming practicum experience. Note: it is welcome but not necessary to post your own references this week in your discussion responses. Enjoy the reading and simply reflect with us.
READ ARTICLE TO ANSWER POST. ( you may site the article) ——–>
Read the article (Links to an external site.)Links to an external site. by:
Kohl, H.W., Craig, C. L., Lambert, E.V., Inoue, S., Alkandari, J.R., Leetonngin, G., & Kahlmeier, K. (2012). The
pandemic of physical inactivity: Global action for public health. The Lancet, 380(9838), 294-305. doi:
http://dx.doi.org/10.1016/S0140-6736(12)60898-8.
A systems approach to dealing with the global issue of physical inactivity is discussed.
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2023 Comment 1 T test The T Statistic is used in a T test running a hypothesis test You
by adminNursing 2023 Please Follow Instructions. Answer Every Question. Al Least 260 Worlds In APA Format. Research From (2013 -2018) Academic References Need.SUBTANTIVE POST Please. Thanks
Comment 1 T test The T Statistic is used in a T test running a hypothesis test You 2023 Assignment
Comment 1
T-test
The T-Statistic is used in a T test running a hypothesis test. You will use T-Statistic with a p value. The p value tells us the odds are that the results could have happened by chance. A big difference in the t-score is that the population standard deviation will need to be estimated (therefore the population standard deviation is unknown until estimated).
T-test is used when: the sample size is below 30 and it has an unknown population standard deviation
Z-test
The Z-score (also known as standard score) lets us calculate the probability of a score occurring within a normal frequency distribution. Also, the Z-score lets us compare two scores from other different normal distributions. This is done by changing scores in a normal distribution to z-scores resulting in a standard normal distribution. In a normal frequency distribution by standardizing the score we can find out the probability of the score. In a normal frequency distribution (bell curve) a z-score is the number of standard deviation from the mean. This is a measure of how many standard deviations above or below the population mean. Z-score results are used to compare results from a test to a normal population (Statistics How to, 2018). For example if you want to compare a student test average to the rest of the class.
Z-test– lets us to decide if the sample is different from the population mean. Four things must be known when using z-score: 1. the population mean 2. The population standard deviation 3. The sample mean and 4. The sample size.
z-test is used when: the sample size is above 30 and the standard deviation of the population is known or if the standard deviation of the population is known but the sample size is small then a t-test is used.
Comment 2
Good morning Instructor and classmates; The statistical procedure for testing whether or not a chance s credible for the explanation of an experimental finding is called hypothesis testing. One of the key concepts in hypothesis testing is that of significance level or, the alpha level, which specifies the probability level for the evidencee to be an unreasonable estimate. (Banerjee, 2009).
An alpha level of 0.05 is the recommended pattern for a two-tailed test. The alpha level is considered based on the individual’s personal conditions of how strong the individual wants the evidence to be. The alpha level is also the probability or p-value that researchers accept as being significant, and can also be interpreted as the opportunity of making a Type I or Type II error. When you set a more stringent (smaller) alpha like such as .01 or .001 it decreases the probability of making Type 1 error, the likelihood of making a Type II error, suggesting that an alpha level .05 is a satisfying compromise between the making of Type I and Type II errors.
For some experiments, if the consequence of the applying of null hypothesis is extremely serious, for example, if null hypothesis applies, there may be death, injury or serious disease happening, then you want to try your best to avoid the type I error, that is, avoid the situation that null hypothesis is true but you reject it. As the significance level is the probability you will make the type 1 error. So for such experiments with serious results, we want to make the level smaller than standard situation. So or such experiments, if you can’t tolerate a 5% (0.05) chance of being wrong, use a lower significance level, 0.01 or 0.001 for example. 0.001 is common if there’s a possibility of death or serious disease.
The first kind of error that involves the rejection of a null hypothesis where it is actually true is called Type I error. Type I errors are equivalent to false positives. For example, A drug like Cyclophosphamide (Cytoxan) that is being used to treat various types of cancer can have dangerous results if the Null hypothesis is rejected because this would claim that this drug does not have any effects on the cancer. However, if the Null hypothesis is true, when the drug ( Cytoxan) does not combat cancer. As a result, the drug (Cytoxan) has falsely claimed to have a positive effect on treating the cancer.
Type II error is equivalent to false negatives. A Type II error occurs if it showed that the drug Cyclophosphamide ( Cytoxan) has no effect on treating the cancer, but in actuality it does. In conclusion, even though Type and Type II errors are part of the process of hypothesis testing and errors can’t be completely eliminated, one type of error may be minimize by decreasing the probability of the type of error in order for the probability of the other type of error to increase.
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2023 Comment 1 P values or significance level is commonly used e g clinical trials to
by adminNursing 2023 Please Follow Instructions. Answer Every Question. Al Least 260 Worlds In APA Format. Research From (2013 -2018) Academic References Need.SUBTANTIVE POST Please. Thanks
Comment 1 P values or significance level is commonly used e g clinical trials to 2023 Assignment
Comment 1
P-values or significance level is commonly used (e.g. clinical trials) to determine whether hypothesis tested (comparison of groups’ characteristics) was significant or not. Often outcomes are labeled with “statistically significant” “non-statistically significant” “unlikely due to chance”, or “due to chance”, and so forth. Researchers make conclusions whether to reject the null hypothesis and accept the alternative hypothesis, vice versa, based on significant level.
Alpha levels are set depending on nature of experiments. Researchers set rate how much error is tolerable. For instance, 0.01 alpha level is more liberal than a conservative 0.001 alpha level. Many clinical trials choose alpha level of 0.05. Those drugs on clinical trial with higher alpha level of 0.05 rarely receive FDA approval. In a Phase II clinical trial, where effectiveness and safety is still being evaluated and there are fewer treatment options (for example for rare type diseases), scientist might set a liberal alpha level (Palesch, 2014). Many higher alpha levels are driven by studies’ cost and lower alpha levels by risks such as possible lifelong disability or death. For instance, in Phase III clinical trial such as testing efficacy of hyperbaric oxygen therapy (HBO) on post stroke patients require a conservative alpha level such as 0.02 (Rusyniak, et al. 2018,) or even more stringent level.
Comment 2
The Greek letter alpha is significant in hypothesis testing. There is not a universal value of alpha that should be used on all statistical tests. Choosing the alpha level is a judgement call of the researcher and it depends on the situation. Raising the alpha level poses more risk to type I and type II errors. Raising the alpha level is
usually related to having a small or limited sample size.
An experiment when the alpha level 0.01l might be used is in a study of a medical screen for a disease. An example, testing for cancer. All possibilities must be considered when the choosing the alpha level, The possibilities that he test may give a false positive report of cancer along with a report of egative when the disease was actually positive but the test did not catch the isease. A false positive will increase the patient’s anxiety leading to other ests and in the end the test was negative. This creates a type 1 error. The patient hat tested negative will not get the treatment that they needed for the isease which can lead to death and this is a type II error. Increasing the alpha level to 0.1 could result n a lower likelihood of a false negative
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We give our students 100% satisfaction with their assignments, which is one of the most important reasons students prefer us to other helpers. Our professional group and planners have more than ten years of rich experience. The only reason is that we have successfully helped more than 100000 students with their assignments on our inception days. Our expert group has more than 2200 professionals in different topics, and that is not all; we get more than 300 jobs every day more than 90% of the assignment get the conversion for payment.
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2023 Comment 1 P values or significance level is commonly used e g clinical trials to determine whether
by adminNursing 2023 Please Follow Instructions. Answer Every Question. Al Least 260 Worlds In APA Format. Research From (2013 -2018) Academic References Need.SUBTANTIVE POST Please. Thanks
Comment 1 P values or significance level is commonly used e g clinical trials to determine whether 2023 Assignment
Comment 1
P-values or significance level is commonly used (e.g. clinical trials) to determine whether hypothesis tested (comparison of groups’ characteristics) was significant or not. Often outcomes are labeled with “statistically significant” “non-statistically significant” “unlikely due to chance”, or “due to chance”, and so forth. Researchers make conclusions whether to reject the null hypothesis and accept the alternative hypothesis, vice versa, based on significant level.
Alpha levels are set depending on nature of experiments. Researchers set rate how much error is tolerable. For instance, 0.01 alpha level is more liberal than a conservative 0.001 alpha level. Many clinical trials choose alpha level of 0.05. Those drugs on clinical trial with higher alpha level of 0.05 rarely receive FDA approval. In a Phase II clinical trial, where effectiveness and safety is still being evaluated and there are fewer treatment options (for example for rare type diseases), scientist might set a liberal alpha level (Palesch, 2014). Many higher alpha levels are driven by studies’ cost and lower alpha levels by risks such as possible lifelong disability or death. For instance, in Phase III clinical trial such as testing efficacy of hyperbaric oxygen therapy (HBO) on post stroke patients require a conservative alpha level such as 0.02 (Rusyniak, et al. 2018,) or even more stringent level.
Comment 2
The Greek letter alpha is significant in hypothesis testing. There is not a universal value of alpha that should be used on all statistical tests. Choosing the alpha level is a judgement call of the researcher and it depends on the situation. Raising the alpha level poses more risk to type I and type II errors. Raising the alpha level is
usually related to having a small or limited sample size.
An experiment when the alpha level 0.01l might be used is in a study of a medical screen for a disease. An example, testing for cancer. All possibilities must be considered when the choosing the alpha level, The possibilities that he test may give a false positive report of cancer along with a report of egative when the disease was actually positive but the test did not catch the isease. A false positive will increase the patient’s anxiety leading to other ests and in the end the test was negative. This creates a type 1 error. The patient hat tested negative will not get the treatment that they needed for the isease which can lead to death and this is a type II error. Increasing the alpha level to 0.1 could result n a lower likelihood of a false negative
#eduessaylab #assignmenthelp #nursingstudents #lawstudents #termpaperbuddy #savvyessaywriters #onlineprowriters #essaywriters4life #exclusivewritings #writinghub.net #collegerpapertutors #www.legalessaywriters.com # legalessaywriters #nursingsavvywriters #nursingassignmenthelp #professionalessaybuddy.com #professionalessaybuddy #timelynursingwriters
We give our students 100% satisfaction with their assignments, which is one of the most important reasons students prefer us to other helpers. Our professional group and planners have more than ten years of rich experience. The only reason is that we have successfully helped more than 100000 students with their assignments on our inception days. Our expert group has more than 2200 professionals in different topics, and that is not all; we get more than 300 jobs every day more than 90% of the assignment get the conversion for payment.
PLACE ORDER NOW