# Determining continuous vs discrete data sets?

When I collect data for my work, I get a set like the following:

[ 133, 183, 185.16, 188, 143, 128, 135.5, 100.55, 117.96, 95.5 ]


These points are part of a continuous scale, but there is no known function associated with them. How does one develop a probability density (continuous) function from this set?

I know I can find higher-order calculations based on this unknown probability density function, such as the value of the Expectation operator on this set by way of a basic average; however, this average requires treating the measurements as discrete values each with the same probability of 1/n, and thus this is managed as a discrete probability distribution function, even though the values are on a continuous scale.

This is unlike most discrete data problem examples, where dice are used and there is no continuous scale (we either have 1, 2, 3, 4, etc.).

This is the oddity that is bugging me: How to determine continuousness vs discreteness. If the function that describes the data is known, then my assumption is we can act on it as a continuous data set, but if not then its all simply discrete?

It is highly unlikely that within the precision of my instrumentation I will get the same value for two measurement events; however, this is entirely possible, so is this data simply a massive sample space of all possible measures (down to some finite precision), where it is possible to have overlap (though not probable)?

The stats books and examples I've dealt with so far love to outline discrete data sets as those with a great deal of obvious event overlap, such as dice, cards, and coins, where the values and events are finite and part of a rather small sample space. For continuous data sets, examples always include a function that describes the probability density over some domain range.

This leaves me wondering about this specific situation, where the measurements are clearly discrete in the sense that they are separate points on a line, but this seems to differ from other examples of discrete events, where in contrast, their values are not continuous.

• These are good things to wonder about. However, many of them lead to non-questions in the sense that most data have no inherent type: your model of the data determines what type they have and you are allowed to contemplate different models using different types. That leaves me scanning through your post to identify any substantial question. Would it be the first one about estimating a density? The second one that the data must be modeled as discrete if they are not modeled as continuous? The third one about modeling measurement error?
– whuber
Commented Apr 29, 2015 at 18:37
• I wonder if you are making a mountain out of a molehill. The sample average can be used as an estimate of the population mean parameter no matter whether the population is continuous (say, Gaussian--$\mu$) or discrete but uncountable (say, Poisson--$\lambda$) or discrete & finite (say, binomial--$\pi$). Commented Apr 29, 2015 at 18:41
• @whuber, Apologies if the question wasn't clear. The second one you mention is more along the lines of what I'm wondering. Here the data is on a continuous scale, but is discretely sampled. Without a model of what happens on the entire range of the continuous random variable, are we left managing it as a discrete data set? How (if possible) would one bridge this to manage the continuous nature of the random variable? It seems this is quite relevant, especially when inferring conclusions for a population as a whole from a sample. Commented Apr 29, 2015 at 18:47
• @gung, I definitely may be ;), but am doing so for the sake of understanding the connection between simply getting the classic "average" of a data set such as the above, and approaching this from the more theoretical standpoint (as is presented in books), such as performing an expectation operation on the probability density/distribution function for this data. I know how to get the average easily, but am not seeing how to handle this as a density or distribution function, and where that distinction lies. Commented Apr 29, 2015 at 18:52
• I'm not quite sure if I follow all of that (@whuber may be able to help you more). Generally, we take the expectation of the theoretical distribution, not sample data. In mathematical statistics classes, you show that the sample average is a consistent estimator, etc. I'm not sure you need to worry about what you are trying to do here. Commented Apr 29, 2015 at 18:59