Statistical tools present critical approaches of determining the relationship between medical variables in healthcare interventions. This paper identifies a health care scenario that adopts the use of confidence intervals in analyzing medical data and the significance of adopting confidence interval techniques in medical data analysis.
Confidence interval interpretation is useful in analyzing the degree of uncertainty associated with medical statistical data. This statistical tool enables clinicians to ascertain the realities of their expected results. For instance, a doctor can use the concepts of confidence interval levels to determine the relationship between a patient’s use of drugs and their response in using those drugs. This concept is also crucial in identifying the causal factors of diseases. One such example is a doctor’s use of confidence interval in determining a causal relationship of whether hypertension is directly linked to obesity. Confidence interval levels are also used in medical researches to describe the characteristics and the parameters of any given population in examining data relating to the adopted medical interventions (Mintab, 2015, p. 10). The practicability and feasibility tests for such interventions are therefore interpreted by using the concepts of probability and confidence levels in ascertaining the level of errors possibly associated with healthcare processes (Zhaomin & Fineout, 2016, p. 7). Healthcare institutions also rely on the confidence levels to measure related outcomes of healthcare procedures in a similar way. By maintaining a standard confidence level, the outcomes of medical procedure are computed with pre-calculated effect size and anticipated relative risks.
Delegate your assignment to our experts and they will do the rest.
A case scenario
A sample of two drugs; Y and Z were tried on patients for their effects on increasing the weight of a patient. 5 patients were given drug Y while 7 patients were given drug Z and their weight in pounds noted as indicated below.
| Drug Y | 6 | 10 | 14 | 7 | 3 | ||
| Drug Z | 8 | 6 | 10 | 13 | 4 | 8 | 7 |
The goal of the experiment was to determine how the two drugs differ significantly with their effects in increasing weight. A significant level of 5% with a two-tailed test standard error of 1.96 was adopted as well as a standard mean variation of V=10.
Statement of null hypothesis
h 0 : μ 1 = μ 2
Statement of alternative hypothesis
H 1 : μ 1 ≠ μ 2
t = 
Calculation of 
, 
and S
| X 1 | X 1 – | (X 1 – ) 2 | X 2 | (X 2 – ) | (X 2 –  ) 2 |
| 6 | -2 | 4 | 8 | 0 | 0 |
| 10 | +2 | 4 | 6 | -2 | 4 |
| 14 | +6 | 36 | 10 | +2 | 4 |
| 7 | -1 | 1 | 13 | +5 | 25 |
| 3 | -5 | 25 | 4 | -4 | 16 |
| 8 | 0 | 0 | |||
| 7 | -1 | 1 | |||
| ΣX 1 = 40 | Σ(X 1 – ) = 0 | Σ (X 1 – ) 2 = 70 | ΣX 2 = 56 | Σ (X 2 – ) = 0 | Σ (X 2 – ) 2 = 50 |
X 1 = 
= 40/5= 8 X 2 = 63/7=8
S=√70/4=4.18 S=√60/9=2.9
S p = 
= 3.406
or 2.9 
= 1.96
t = 
= 
= 0.50
Decision rule
For any standardized mean less than 1.96, the null hypothesis is accepted while the alternative hypothesis is rejected (Avijit, 2017). On the contrary, for any standardized mean above 1.96, a null hypothesis is rejected and the alternative hypothesis accepted.
Conclusions
At V=10, 0.5<1.96. The null hypothesis is accepted while the alternative hypothesis is rejected. Accepting the null hypothesis suggests a no difference in efficiency between the two drugs with respect to their influence on weight. The use of confidence intervals in analyzing medical data is therefore important. The confidence intervals is significant in measuring the expected medical outcomes as well as ascertaining the causal factors of other medical conditions like in the case of using obesity results to determine their level of influence on patients suffering from hypertension.
References
Avijit, H. (2017). Using the confidence interval confidently. Retrieved from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5723800/
Mintab (2015).. Understanding Hypothesis Tests: Confidence Intervals and Confidence Levels . Retrieved from: https://www.google.com/amp/s/blog.minitab.com/blog/adventures-in-statistics-2/understanding-hypothesis-tests-confidence-intervals-and-confidence-levels%3fhs_amp=true
Zhaomin, H. & Fineout, E. (2016). Understanding confidence intervals helps you make better clinical decisions. Retrieved from: https://www.myamericannurse.com/understandingconfidence-intervals-helps-make-better-clinical-decisions/
