HYPOTHESIS TESTING FOR DIFFERENCES BETWEEN GROUPS 1
MHA5017
Hypothesis Testing for Differences Between Groups
Capella University
Data Analysis for Health Care Decisions
MHA5017
Hypothesis Testing for Differences Between Groups
Hypothesis testing is the process of determining from a sample whether something could
be true of a population in other words, inferring from a sample that may be true in a population
(Kros & Rosenthal, 2016). In this assignment, an investor needs to make a decision on whether
to acquire one of two medical clinics based on their productivity, as measured by the total
number of visits per month. A t test will be utilized to assess whether a value found from a
sample could have come from a population in which a hypothesized value is true (Kros &
Rosenthal, 2016). Hypothesis testing, will determine if p is less than 0.05 (alpha) or if p is
greater than 0.05 (alpha). The determination will be based off of if the results are significant or
non-significant. According to Gallo (2016), statistical significance helps quantify whether a
result is likely due to chance or to some factor of interest. When a finding is significant, it
simply means you can feel confident that it is real. For significant results, if the p value is less
than 0.05 you would then reject the null hypothesis. For a non-significant result, the p value
would be greater than 0.05, resulting in not rejecting the null hypothesis. A null hypothesis
proposes no differences between variables of a population.
Generate hypothesis
In order to reach statistical decisions, broad statements or hypotheses are formed about
the probability distribution of the population. Hypothesis testing is the formal statistical process
used to evaluate the probability or likelihood a hypothesis is true (Frey, 2018). The investor will
