Please provide at least 150-word response to the post below to continue conversation and provide new information/content. Be sure to research/cite/reference sources in each discussion.

1st Post: Inferential statistics help researchers use data from the representative sample to make inferences (assumptions) about the population from which the sample was taken (Frost, 2022). Consequently, if a researcher does not need to make inferences, then inferential statistics is not necessary. For example, in qualitative studies, research is focused on the specific experiences of a small number of people and those experiences could not be quantified or extrapolated to the entire population. 

Two significant reasons for using inferential statistics are making estimations about the population (such as figuring mean SAT scores for high school graduates) or testing research hypotheses to draw conclusions about populations (for example, figuring relationships between SAT scores and college admission rates) (Bhandari, 2023). 

The inferential statistics are inappropriate if I want to discuss data from a few collected cases. The studied cases do not represent the entire population; they represent specific observations or individuals. The census information (gender, ethnicity, highest degree earned) describes the 
entire population of interest (such as information about all US residents); therefore, we do not need to make inferences. In the third example, we have a data set that includes standardized test scores from the annual achievement test. Since we have data about 
each student who took the test, we do not need to make inferences (we already have all the necessary information). 

In last week’s assignment, I read about the study conducted by Miller et al. (2023), where researchers tried to determine whether nursing residency programs (NRPs) improve nurses’ transition to practice compared to traditional orientation programs (TOPs). Researchers enrolled participants into intervention (NRPs) and control (TOPs) groups. They recruited 77 participants into the intervention and 29 into the control group. They used multiple electronic surveys to compare the two groups over the year. Researchers tested differences between the two groups using Pearson’s χ 2 for categorical variables and Student’s t-test for continuous variables. The results showed statistically significant differences between the two groups, where NRP students significantly improved their readiness for practice, retention, and job satisfaction compared to the control group. This example shows that researchers used the results from two small samples to make inferences about the entire population (nurses transitioning to practice).