ukrida

We often regard the society we are living in as a population, but what is exactly defining a population? In a layman term, we may surmise population as all observable subjects inhabiting a certain location. In research context, this interpretation may not present as the least imperfect definition, but we can generalize the idea of “subjects” into entities of interest. Parameters are quantitative summaries, where we derive a general value in interpreting certain aspects of such a population.

To comprehend our surroundings, we rely on processed sensory inputs and infer the best action to respond with. In a more complex (or complicated) situation, we often find recorded inputs more reliable than our sole perception. Recorded inputs are data, which may or may not follow a certain structure. In essence, data is distinguishable based on their properties, resulting in different data types. With more collected data, a particular pattern emerges, where we can observe a peculiar behaviour from one pattern compared to the other. Here we discuss the nature of data, viz. its type and distribution.

Researchers ought to conduct a thorough and objective observation. In doing so, we may start from formulating a question and hypothesis prior to making prediction, performing experiments, and analyzing results. Hypotheses are different from theory, where we need evidence to establish the fact revolving around proposed hypotheses. A research or investigation is a mean to substantiate evidence and support the hypothesis. Thus, hypothesis needs to comply with cases and variables introduced in a study.

Proving or disproving a research hypothesis requires representative evidence which may help us substantiate our claims. In general, we need a large amount of data following a rigorous and well-documented procedure. However, inviting all potential subjects is not feasible due to time and resource constraint. Considering the amount of limitation we have, how do we determine the minimum required amount of data to answer our research question?

In statistics, proving or rejecting an assumption requires a rigorous formal approach, which highly depends on the formulated hypothesis. The current post will help us in conducting a statistical test to measure hypothesized proportional difference among categorical variables. Another common name assigned to such a formal step is the test of independence. We shall see various tests we have, how they work and when to use them.