With quantitative analysis, it is especially important to understand the units of measurement in comprehensible formats, such as visual representations. Graphs, charts, plots, and histograms adequately display raw data for a given context and chances of remembering visuals are greater than remembering numbers or text. Acquiring this working knowledge also includes skills in understanding scales and distributions. Truth and inquiry are a process related to logic, evidence, and argument as Trochim will elaborate on in his course presentations and Knowledge Base. Human beings interpret raw data and there are general guidelines presented to evaluate assertions and to assess validity.
Quantitative strategies use normal distributions based on statistical or regression analysis.
However, if the constructs used to establish causality are not clearly operationalized to begin with, then those inferences about relationships and variables may not be valid or reliable. It is logical to improve construct validity in order to strengthen internal and conclusion validity. Types I and II errors are firmly established in probability theory that one or the other hypothesis is the incorrect conclusion arrived at in the research. Basically, a researcher wants to stat istically demonstrate that their program did have an effect in order to accept the alternative hypothesis. Simultaneously, understanding the theoretical background within the research construct can help eliminate systematic bias early on.