minimal 2 references, 250 words each question (pls answer succinctly )
1. Descriptive statistics, such as demographics of the sample, are required for the DPI Project (implementing the ABCDEF bundle). Describe the project population (adult patients 18 years of age and older) and project sample (35 participants). What demographic variables of your sample will you collect? (age, sex, race, insurance, transferring facility, code status), What data source will you use to get these data? (electronic health record, LTRAX- transferring facility data)What do these data tell you about your sample? risks of comorbidites, likely to wean, decannulation; provide evidence supporting your response.
2. compare parametric and nonparametric statistical tests. How does each test depend on the assumption of normality? What would you do if the data are not normally distributed? Provide evidence supporting your response.
Expert Solution Preview
1. The DPI Project aims to implement the ABCDEF bundle in adult patients 18 years of age and older, with a sample size of 35 participants. The demographic variables of the sample that will be collected include age, sex, race, insurance, transferring facility, and code status. The data source to collect these variables will primarily be from the electronic health record and LTRAX- transferring facility data. These data will help identify the risks of comorbidities in the sample and also the likelihood of the patients to wean and decannulation. The collected data will help in analyzing the patient characteristics and developing strategies for improving patient care. Research has shown that demographic data collection is essential in evaluating the effectiveness of the ABCDEF bundle, which is aimed at improving patient care in adult populations (Pun et al., 2021).
2. Parametric and nonparametric statistical tests are commonly used in medical research. Parametric tests assume that the data follow a normal distribution, whereas nonparametric tests do not require this assumption. The parametric test requires normality for optimal results, and violations of this assumption can lead to incorrect conclusions. In contrast, nonparametric tests can be used when the data are not normally distributed or when the data are ordinal or nominal. To deal with non-normality data, nonparametric tests, such as the Wilcoxon rank-sum test and Mann-Whitney U test, can be used. These tests are based on ranking observations rather than on their actual values (Islam et al., 2021).
In conclusion, the collection of demographic data is essential in evaluating the effectiveness of medical interventions. Moreover, the use of parametric and nonparametric tests is dependent on the distribution of the data, and if the assumption of normality is not met, nonparametric tests can be used as an alternative.