Secondary analysis is not the best term to describe the outcome of this procedure. Secondary analysis is a type of research design that uses existing data from a primary source or study to answer a new or different research question. Secondary analysis can be a valid and efficient way of reusing or repurposing data for new purposes or perspectives, as long as the researcher obtains permission from the original source, acknowledges the limitations and strengths of the data, and follows ethical and scientific guidelines for conducting and reporting research. Secondary analysis is not the same as recalculating or manipulating data to obtain biased findings.
 

Theoretical differentiation is not the best term to describe the outcome of this procedure. Theoretical differentiation is a process of developing or refining a theory based on empirical data and logical reasoning. Theoretical differentiation involves identifying and defining the concepts, constructs, propositions, and relationships that constitute a theory, as well as testing and validating them using various methods and sources of evidence. Theoretical differentiation can enhance the clarity, consistency, coherence, and applicability of a theory. Theoretical differentiation is not the same as recalculating or manipulating data to obtain biased findings.
 

Biased findings are the best term to describe the outcome of this procedure. Biased findings are the results of a data analysis that are influenced or distorted by the researcher's personal beliefs, preferences, expectations, or interests. Biased findings do not reflect the true or accurate nature of the data or the phenomenon under study. Biased findings compromise the validity and reliability of the research, as well as its ethical standards and scientific integrity. Biased findings can occur when the researcher intentionally or unintentionally manipulates or misrepresents the data, methods, analysis, or interpretation to support a desired or predetermined conclusion. In this case, the nurse-researcher is dissatisfied with the results of a data analysis and recalculates the statistical computations applied to the data for the purpose of obtaining results that are more supportive of the researcher's personal belief system. This is an example of biased findings, as the nurse-researcher is altering or falsifying the data analysis to fit their own agenda or bias.


The other options are not correct because:
 

Non-parametric data analysis is not the best term to describe the outcome of this procedure. Non- parametric data analysis is a type of statistical analysis that does not assume or require that the data follow

a specific distribution or meet certain parametric criteria, such as normality, homogeneity, or independence. Non-parametric data analysis can be used when the data are ordinal, nominal, skewed, or have outliers. Non-parametric data analysis can provide robust and flexible tests for comparing groups or examining relationships among variables. Non-parametric data analysis is not the same as recalculating or manipulating data to obtain biased findings.