Statistical Analysis

Running head: STATISTICAL ANALYSIS
Statistical Analysis
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STATISTICAL ANALYSIS
Answer 1: Will Red Wine Make the Difference?
Answer a)
It has been observed from studies originating in France around 15 years back that moderate red
wine drinking (1-2 glasses per day) lowered rates of heart disease.
The most appropriate method of testing this theory would be hypothesis testing through
experimental research design. In this method, the impact of moderate red wine drinking on heart
disease rates can be examined through experimental and quantitative study. Two groups of
sample will be chosen, and only one group will consume 2 glasses of red wine each day for the
study period. Pre and post examination, data will be collected and tested to see the impact of red
wine consumption on the heart disease.
Primary data would be collected on gender, age, average red wine consumption per day,
presence of heart diseases, and perception regarding the condition of the heart disease pre and
post examination. 100 people will be chosen for the primary data collection and they will be
divided into two equal groups of 50 people. The sample is collected from the database of cardiac
patients from a hospital.
Answer b)
In case of observational data collected from big datasets, the dependent variable (Y)
would be rate of heart disease among the patients, and independent variable (X) would be
average consumption of red wine per day.
Answer c)
Sampling error can occur in this case as the population size is too large compared to
sample size and types of heart disease are different with varying characteristics. In other words,

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STATISTICAL ANALYSIS
there might be difference between sample and population parameter values, resulting in sampling
error (Chalmer 2020). Not all cardiac issues can be lessened by consuming red wine and the
level of complication is also a factor that could influence the impact of red wine. Hence, the
accuracy of the outcome can be hampered as the sample might include the above mentioned
cases also.
Answer d)
Confounding variable is one having an impact on the dependent variable apart from the
independent variable(s) (Satten, Kong and Datta 2018). Here, per day average consumption of
red wine is the independent variable, and hence, red wine is not a confounding variable.
However, age and gender would be confounding variables in this case as level of heart disease
and red wine consumption vary by age and gender.
Answer e)
External validity refers to the consistency of the data in outside contexts, gathered to
address the research issue. In other words, external validity can be defined as the ability of the
data to produce consistent generalized outcome in different situations and for different samples
(Bertanha and Imbens 2019). Extrapolation refers to the speculation or estimation of an event on
the basis of the facts and observations (Barberis et al. 2018). In case of different sample groups
in the USA, the concepts of external validity and extrapolation are concerns as different samples
in different regions would have different characteristics and different types of heart disease
which might not be affected by red wine consumption.