Im a nurse that works in a hospital. What are some examples of Inferential statistics used in the workplace?
http://www.socialresearchmethods.net/kb/statinf.php
from one supplier, 30 percent of the syringes are not sealed well, and 10 percent of the nettles are mislabled for size. Inference: all other products need to be examined.
Kids in the emergency clinic have a mean body mass index of 35, with a deviation of 4.
Inference: fat kids are having more medical problems than normal.
Inference means to extend beyond the actual data.
As a nurse working in a hospital, inferential statistics can be a valuable tool for analyzing and drawing conclusions from the data you encounter. Here are a few examples of how inferential statistics can be used in your workplace:
1. Hypothesis testing: One common application is testing hypotheses about various healthcare interventions or treatments. For instance, you may want to investigate whether a certain medication is effective in reducing patient pain levels. You could collect data from two groups of patients, one receiving the medication and the other receiving a placebo, and then use inferential statistics to determine if the medication indeed has a significant effect on pain reduction.
2. Confidence intervals: Inferential statistics can help estimate the range within which a population parameter is likely to fall. For example, you might want to determine the average length of hospital stays for patients with a specific medical condition, but you cannot collect data from every patient. By calculating a confidence interval, you can estimate the true population mean length of stay based on a sample, along with the level of confidence you have in this estimate.
3. Regression analysis: This technique allows you to explore relationships between variables and make predictions. You could use regression analysis to examine the relationship between patient age and recovery time, for example. By fitting a regression model, you can estimate how much recovery time increases or decreases based on age, which can help inform decision-making and allocate resources more effectively.
4. Survival analysis: In a hospital setting, survival analysis is often used to study patient outcomes over time. This technique can help estimate survival probabilities and analyze factors influencing survival rates. For instance, you could investigate the impact of different treatment protocols on patient survival after a specific surgery.
These are just a few examples, but inferential statistics can be applied to various situations in healthcare settings. It's important to identify the research question or problem, collect relevant data, and then choose the appropriate inferential statistical method to analyze and interpret the results.