Six Types of Questions

Six Types of Questions


There are six types of questions you can ask in a data analysis
  1. Descriptive
  2. Exploratory
  3. Inferential
  4. Predictive
  5. Causal
  6. Mechanistic

Descriptive

The fundamental feature of a descriptive question is that 
you're often looking to summarize a characteristic of a dataset, so often this 
involves taking the average or taking the proportion of some feature in your data. 
So for example, 
you might ask how many people have visited this website in the last 24 hours? 
Or, what's the average level of air pollution in the city of Baltimore? 
So here, you're summarizing kind of the features of a dataset and 
you're focusing on the data that you have on hand. 
And you're not really worried about things that are outside of the dataset yet. 
You really just want to summarize the numbers that you have.

Exploratory

The basic goal here is you want to look at trends or 
relationships between variables in your data set. 
Sometimes, these are called hypothesis generating types of analysis because 
you're looking at the dataset that you have in hand and looking for 
relationships that might be of interest. 
Again, like a descriptive question, typically with an exploratory question, 
you're not interested in things that are outside the dataset, but 
rather in summarizing and characterizing relationships within a dataset.

Inferential

Inferential question can often be the result of lots of exploratory and 
descriptive types of analyses. 
And the fundamental property of an inferential question 
is that you wanna make a statement about something outside the dataset. 
And so you often, for example, you wanna know whether a relationship that you 
observe in the dataset holds somewhere else. 
Either in another dataset or a different population of kind of data points. 
And so, the key to an inferential question is that you wanna make a statement about 
something that you don't observe. 
And so this is a much more difficult type of question because now you're 
concerned about things that are outside the dataset. 
And so you have to be careful of what types of methods and 
what types of approaches you use there.

Predictive

with a predictive question you wanna know whether you can take a set 
of features and use them to predict another feature on a given person or 
on a given unit of analysis, right. 
So this is at a large scale about 
essentially looking at correlations between lots of features in a data set.

often with predictive types of questions, 
they lead you to solutions that don’t necessarily tell you how things work or 
explain the mechanism of what’s going on inside any given system. 
Because the goal is to really produce a very good prediction of a given feature, 
given a set of other features, and 
the goal is not really to explain how things are working. 
Now on occasion, a predictive question can lead you to an explanation about what's 
going on, but the key point here is that it's not the ultimate goal.

Causal

with a causal question we're often looking to determine how average 
changes in a set of features or 
in a given feature will change when we modify another feature. 
And so, if we take a variable and deliberately make changes to it, 
on average, how will another feature or another characteristic be affected? 
These types of questions can often be directly addressed via experiment. 
Such as randomized controlled trials, 
or directly controlled kind of laboratory experiments.

However, in many other situations, causal questions can really only be 
answered indirectly using observational data, so in situations where we 
can't control what the settings are or what the experiment or design a specific 
experiment to kind of to collect data, on directly on this question. 
And so, there in situations like that, 
we need to accumulate evidence through many different types of studies, and 
to develop a pattern that would suggest that a causal relationship exists.

Mechanistic

The sixth type of question that we are interested in is a mechanistic type of 
question. 
And the goal here is essentially to uncover a deterministic link 
between two sets of features, okay? 
And so we want to know if we can, if we change one measurement on one hand, can 
we, does it always result in a specific outcome on a different measurement? 
Okay? 
Now this type of relationship is often very difficult to identify outside of 
highly controlled environments, for example in engineering processes. 
So we're often not, we're not often gonna be looking at mechanistic types of 
questions, but it is an important area to think about.

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