Question

the name of the course is health planning

In maximum 2 pages length, 1.5 line spacing, 12 Times New Roman font, answer the following questions

Questions:

Define data cleaning and why it is important. (4 points)

Discuss Statistical significance and clinical significance. (4 points)

What is the Ecological fallacy and provide one example? ( 4 points )

Answer 1

1. Data cleansing or data cleaning is the process of detecting and correcting (or removing) corrupt or inaccurate records from a record set, table, or database and refers to identifying incomplete, incorrect, inaccurate or irrelevant parts of the data and then replacing, modifying, or deleting the dirty or coarse data. Data cleansing may be performed interactively with data wrangling tools, or as batch processing through scripting.

cleansing techniques are usually performed on data that is at rest rather than data that is being moved. It attempts to find and remove or correct data that detracts from the quality, and thus the usability, of data. The goal of data cleansing is to achieve consistent, complete, accurate, and uniform data. Data cleansing leads to high quality data. When data is of excellent quality, it can be easily processed and analyzed, leading to insights that help the organization make better decisions. High-quality data is essential to business intelligence efforts and other types of data analytics, as well as better overall operational efficiency.

2.  

Significance is defined as the quality of being important. In medicine, we distinguish between statistical significance and clinical importance.

Statistical Significance. Medical studies are carried out on selected samples of people, but the goal is to apply the findings to another population (e.g.,  patient ). Naturally, a concern is that the sample used in the study could provide misleading results. Perhaps it was a very small sample; perhaps it was a biased sample that is not equivalent to the people you are treating; perhaps the sample was large enough, but by chance or bad luck it contained people who gave wacky results.

Statistical significance considers the first and third of these concerns. The middle one, bias, cannot be detected by mathematical deductive logic: it needs detailed information on the way the sample was chosen. This is dealt with in the notes on bias.

Clinical significance, or clinical importance: is the practical importance of the treatment. It Is the difference between new and old therapy found in the study large enough for alter the practice Because there is always a leap of faith in applying the results of a study to your patients (who, after all, were not in the study), perhaps a small improvement in the new therapy is not sufficient to cause you to alter your clinical approach. Note that you would almost certainly not alter your approach if the study results were not statistically significant