i sent you 6 Discussions that were posted by my class mates. i just want to send them response on the base of their writing as a discussion. 150 words each and no references necessary.
Michelle_Michy
20200618203836discussion_responses1.docxDiscussion response 1
1- When conducting quantitative research, you are attempting to answer a research question or hypothesis that you have set. One method of evaluating this research question is through a process called hypothesis testing, which is sometimes also referred to as significance testing. The first step in hypothesis testing is to set a research hypothesis.
In statistics terminology, the people in the study are the sample and the larger group they represent is called the population. For example, a sample of statistics students in a study are representative of a larger population of statistics students, you can use hypothesis testing to understand whether any differences or effects discovered in the study exist in the population. Hypothesis testing is used to establish whether a research hypothesis extends beyond those individuals examined in a single study.
Another example could be taking a sample of 200 breast cancer patients to test a new drug that is designed to eradicate this type of cancer. As much as you are interested in helping these specific 200 cancer patients, the real goal is to establish that the drug works in the population for all cancer patients.
In order to undertake hypothesis testing, the research hypothesis should be expressed as a null and alternative hypothesis. The null hypothesis and alternative hypothesis are statements regarding the differences or effects that occur in the population. The evidence is tested against the null hypothesis. When considering whether to reject the null hypothesis and accept the alternative hypothesis, consider the direction of the alternative hypothesis statement. The alternative hypothesis tells us two things. First, what predictions did we make about the effect of the independent variable(s) on the dependent variable(s)? Second, what was the predicted direction of this effect (Laerd Statistics, nd)?
A two-tailed prediction means a choice is not made over the direction that the effect of the experiment takes. It simply implies that the effect could be negative or positive. A one-tail prediction usually reflects the hope of a researcher rather than any certainty that it will happen.
If the statistical analysis shows that the significance level is below the set cut-off value (e.g., either 0.05 or 0.01), the null hypothesis is rejected and the alternative hypothesis is accepted. If the significance level is above the cut-off value, we fail to reject the null hypothesis and cannot accept the alternative hypothesis. You cannot accept the null hypothesis, but only find evidence against it.
Since the hypothesis is the question the researcher wants to answer, the clinical inquiry in healthcare, the research design, how the data is gathered and analyzed is determined by the question or hypothesis (Ambrose, 2018). In healthcare, we aim to find correlations and answers within the data to provide for better patient population outcomes.
2- Hypothesis testing is a way of statistically testing meaningful results or if the results happened by chance. The problem in a hypothesis test is to decide to reject the null hypothesis in favor of the alternative hypothesis.
There are two ways of testing the hypothesis, one-tailed test, and two-tailed test (Ambrose, 2018). A one-tailed test is used when a researcher is certain of the direction in which the data will go. It includes right and left-sided tails depending on if the alternative hypothesis is written as > or <. The two-tailed test is used when researchers are looking to determine the differences between the groups being compared. It is used when the alternative hypothesis is written as not equal.
The testing hypothesis requires several steps. The first step begins with expressing the hypothesis as a null or alternative hypothesis. The null hypothesis is also called the false hypothesis and is the currently accepted value for a parameter. The alternative value is also called the research hypothesis and involves the claims to be tested. The next step is to find the likelihood of the sample result if the null hypothesis were true. This probability is called the p-value. A low p-value would indicate that the sample result would be unlikely if the null hypothesis were true and would lead to the rejection of the null hypothesis. A high p-value means that the sample results would be likely if the null hypothesis were true and leads to the retention of the null hypothesis. The use of the level of statistical significance is used to determine where to draw the line on making the decision how low a p-value should be for rejection of the null hypothesis.
Finding critical values include the use of z-value or t-value. The use of t- value includes a small population sample of less than 30. Critical values will also produce a region of rejection. The next step is to find the test statistics. If the test statistic is greater than the region of rejection, the null hypothesis is rejected.
Hypothesis testing is important in determining if patient education is effective in treating type 2 diabetics. The researcher desires to know if patient education on type 2 diabetics makes a difference in the patient’s knowledge of type 2 diabetes. The null hypothesis is patient education does not change the knowledge level of the participants. The alternative hypothesis is that patient’s knowledge of type 2 diabetes improves following patient education.
3- Hypothesis testing is a way of statistically testing meaningful results or if the results happened by chance. The problem in a hypothesis test is to decide to reject the null hypothesis in favor of the alternative hypothesis.
There are two ways of testing the hypothesis, one-tailed test, and two-tailed test (Ambrose, 2018). A one-tailed test is used when a researcher is certain of the direction in which the data will go. It includes right and left-sided tails depending on if the alternative hypothesis is written as > or <. The two-tailed test is used when researchers are looking to determine the differences between the groups being compared. It is used when the alternative hypothesis is written as not equal.
The testing hypothesis requires several steps. The first step begins with expressing the hypothesis as a null or alternative hypothesis. The null hypothesis is also called the false hypothesis and is the currently accepted value for a parameter. The alternative value is also called the research hypothesis and involves the claims to be tested. The next step is to find the likelihood of the sample result if the null hypothesis were true. This probability is called the p-value. A low p-value would indicate that the sample result would be unlikely if the null hypothesis were true and would lead to the rejection of the null hypothesis. A high p-value means that the sample results would be likely if the null hypothesis were true and leads to the retention of the null hypothesis. The use of the level of statistical significance is used to determine where to draw the line on making the decision how low a p-value should be for rejection of the null hypothesis.
Finding critical values include the use of z-value or t-value. The use of t- value includes a small population sample of less than 30. Critical values will also produce a region of rejection. The next step is to find the test statistics. If the test statistic is greater than the region of rejection, the null hypothesis is rejected.
Hypothesis testing is important in determining if patient education is effective in treating type 2 diabetics. The researcher desires to know if patient education on type 2 diabetics makes a difference in the patient’s knowledge of type 2 diabetes. The null hypothesis is patient education does not change the knowledge level of the participants. The alternative hypothesis is that patient’s knowledge of type 2 diabetes improves following patient education.
4- Statistics is used to draw a conclusion about a population with the data that has been collected. Once a hypothesis is developed, data that has been collected from the sample population is used to determine if the findings meet the hypothesis. Studies are conducted using a specific sampling, but that is different than developing a hypothesis which is developed from an entire population.
A study determined that compassion in care has declined. The study was able to “hypothesize that compassionate care is beneficial for patients (better outcomes), health care systems and payers (lower costs), and health care providers (lower burnout)” (2017, Mazzarelli, Roberts, Trzeciak). They wanted to establish compassionate care as an evidence-based practice. It was determine that “compassion and human connection can promote long-term resilience and well-being” (2017, Mazzarelli, Roberts, Trzeciak) not only for the patient but for the health care provider too.
A study determined that elderly patients who had worked as caregivers retained a higher level of cognitive function as they aged. “Fredman's team set out to test a common assumption that stress among caregivers results in poorer health outcomes when compared with non-caregivers” (2013, Hill). It was determined that those who were caregivers performed at the level of a younger age.
The Null Hypothesis is a to decide between two interpretations of the statistical relationship in a sample during a study. The null hypothesis suggests that there is relationship within the sampling. If it can be determined that there is no relationship between samples then the null hypothesis must be rejected and use the alternative hypothesis.
The use of evidence-based practice has proved that patients have a better outcome from their hospital stay. We use concepts related to research such as side effects from medications, expected outcomes from procedures, and safety such as intentional rounding to prevent falls.
5.2 - Confidence intervals and hypothesis testing are similar as they are both inferential methods that solely rely on approximated sampling distribution. The confidence interval represents a certain percentage of the survey from the sampled population and they are often used with a margin of error. The margin of error will indicate the uncertainty that surrounds the estimated sample population. The researcher has a certain level of confidence in mind that the results from the survey would reflect what the researcher expected to find if in a case it was possible for one to survey the entire population. In hypothesis testing, there is an evaluation of the strength of evidence from the sample population. These, therefore, show that hypothesis testing and confidence interval are inferential methods that purposely depend on the estimated sample population. The clinical significance determines whether the research has a practical application to an individual or group. With the use of a confidence interval, the null hypothesis can be rejected from the data obtained. The range of values determines the rejection in that if the confidence value is 95%, it leads to the acceptance or the rejection of the hypothesis. The confidence interval estimates a parameter, and a hypothesis testing assesses the evidence in the data against one claim and in favor of another.
Supposing a particular treatment reduced the risk of death compared to placebo with an odds ratio of 0.5 and a 95% confidence interval of 0,2 to 0.8. This means that, in the sample, the treatment reduced the risk of death by 50% compared to placebo, and the reduction risk lies between 20% and 80%.
A study was carried out on the effects of controlled cord traction in the third stage of labour on a postpartum haemorrhage. The control treatment was a standard placenta expulsion. Participants were women 18 years or older with a single fetus at 35 or more weeks gestation and planned vaginal delivery. The women in both groups were given prophylactic oxytocin just after birth. The outcome was 2005 women allocated to intervention ( controlled cord traction), 196 (9.8%) experienced postpartum hemorrhage compared with 206 of 2008 allocated to control (10.3%). The reduction in risk of postpartum hemorrhage associated with intervention was not significant (relative risk 0.95; 95% confidence interval 0.79 to 1.15). The use of controlled cord traction had no significant effect on the incidence of postpartum hemorrhage.
In a clinical trial of a new "wonder drug" for rheumatoid arthritis, the remission rates turn out to be 5% higher in those taking the new drug than in those taking the standard drug. The null hypothesis will be Users of the new wonder drug are statistically more likely to experience remission than users of the standard drug. However, is a difference of 5% clinically significant in a population of about 5,000 women? The confidence interval range will determine whether the hypothesis will be rejected or accepted.
An example of a workplace idea would be early ambulation in postoperative patients prevents the risk of developing venous thromboembolism (VTE). In a study, the rate of VTE was lower among patients who could walk, when compared with those who were immobile (10.6% vs 19.7%; p=0.03). The study showed that patients with initial immobility who walked early were still at risk of developing VTE. The risk can be reduced by the combination of ambulation and administration of enoxaparin.
6.2 - Hypothesis testing is the basis for all scientific research. In health care, the use of hypothesis testing determines improvements in patient outcomes The direction for conducting a study is influenced by the hypothesis developed. The proposed hypothesis needs to be tested and there are two ways for testing the hypothesis. A hypothesis can be evaluated using a one-tailed or a two-tailed test. One-tailed testing is the case when the investigator is certain in which the data or the information will go. Whereas in a two-tailed test, the investigator is looking to identify the gaps between the groups that are being evaluated. The importance of a two-tailed test is that here we can identify the positive and negative impacts.
The confidence interval is a gap estimate for the mean. These are a group of values that are kept close to the mean, which can be either in a positive or negative direction. The range of values decides the effect. The rejection and non-rejection of the null hypothesis are based on the 95% confidence interval. A 95% confidence interval means that the research conducted will be 95% close to the true mean, but the other 5% means that there a chance of 5 in 100 that the research can go wrong. Confidence intervals are comprised of the point estimate and a margin of error around that point estimate. The margin of error indicates the amount of uncertainty that surrounds the sample estimate of the population parameter. The CI reflects the risk of the researcher being wrong. Reducing the confidence interval increases the risk of error (Ambrose,2018). Confidence intervals provide information about a range in which the true value lies with a certain degree of probability, as well as about the direction and strength of the demonstrated effect. The size of the confidence interval depends on the sample size and the standard deviation of the study groups. If the sample size is large, this leads to "more confidence" and a narrower confidence interval (Du Prel, Hommel, Rodrig & Blettner,2009).
An example of the use of both hypothesis testing and confidence interval can be seen in a study to determine the impact and sustainability of the effect of chlorhexidine bathing on central venous catheter-associated bloodstream infection. Chlorhexidine bathing has been associated with reductions in healthcare-associated bloodstream infection. Compared with pre-intervention, during the active intervention, there were significantly fewer central venous catheter-associated bloodstream infections (6.4/1000 central venous catheter days vs 2.6/1000 central venous catheter days, relative risk, 0.42; 95% confidence interval, 0.25-0.68; P<.001) (Monticalvo et al, 2012).
