# Discrete and Continuous Data’ Mixture Possibility

Arranging information for the further analysis is an essential stage of any research, hence a variety of taxonomies for information pieces retrieved in the course of the data collection process. Therefore, the precision of the research results depends heavily on the way, in which the data is classified, as well as the methods, in which the data in question is incorporated into the analysis. Although discrete and continuous data types are typically viewed as quite different from each other, a combination of both can be used in research as a means of retrieving the results that are both meaningful and objective.

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To understand the slight differences between the types of data in question, one should consider the current definitions thereof. According to the existing research data taxonomy, discrete data includes a finite number of quantitative variables that can be measured; in other words, they have a specific limit. Therefore, discrete variables can be viewed as a set of dots on a chart.

Continuous variables, in its turn, is traditionally identified as the infinite number of values that are represented as a continuous set of quantitative data (Diggle, Heagerty, Liang, & Zeger, 2013). In other words, the subject matter includes an array of data that cannot possibly be counted. It should be noted, though, that, similarly to the discrete data, continuous one can be measured (Leon & Chough, 2013).

As seen from the descriptions provided above, the primary difference in the two types of data concerns the quantitative evaluation thereof. Discrete data can be counted, yet continuous data can only be measured. Herein lies the difference between the two types of data.

One might conclude that discrete data is finite, whereas continuous data is not. On the one hand, the assumption in question can be deemed as highly logical and, therefore, entirely true. Indeed, seeing that a discrete variable implies that there is a finite number of possibilities, it is logical to consider it finite. However, the limitation in the number of possible outcomes does not define the variable itself. Therefore, a discrete variable can take an infinite number of possibilities that can be enumerated (Shanmugam & Chattamvelli, 2015).

Assuming that a variable incorporating discrete and continuous qualities could exist would be quite a stretch. However, one might consider the following experiment on data generation. Supposing, there are two types of data generation, i.e., a dice (possibilities limited to 6) and an online random variable generator (possibilities unlimited). The choice of one of the two types is decided on the flip of a coin (“heads” mean using the dice, whereas “tails” mean using a generator). In the superposition, the type of data to be chosen can be deemed as both discrete and continuous (Remenyi, Onofrei, & English, 2015).

However, the situation mentioned above is hardly typical. Quite on the contrary, it can be viewed as a singular case. Therefore, it should not be considered as a doubtless proof of the fact that data can be both discrete and continuous. Instead, it needs to be interpreted as the phenomenon that points to the flexibility of the current data classification system (Groebner, Shanon, & Fry, 2014).

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Therefore, a single piece of information retrieved in the course of gathering data for the study can be both discrete and continuous; moreover, it is possible to include both types of data into the analysis so that the outcomes of the research could be deemed as valid.

## Reference List

Diggle, P., Heagerty, P., Liang, K.-Y., & Zeger, S. (2013). Analysis of longitudinal data. Oxford, UK: OUP.

Groebner, D. F., Shanon, P. W., & Fry, P. C. (2014). Business statistics. (9th ed.). New York, NY: Pearson.

Leon, A. R., & Chough, K. C. (2013). Analysis of mixed data: Methods and applications. New York, NY: CRC Press.

Remenyi, D., Onofrei, G., & English, J. (2015). An introduction to statistics using Microsoft Excel (2nd ed.). New York, NY: Academic Conferences and Publishing Ltd.

Shanmugam, R., & Chattamvelli, R. (2015). Statistics for scientists and engineers. New York, NY: John Wiley & Sons.

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