A Math? A Science? An Art? Or Something Else?
With an increasing use of data to make decisions, Statistics has been essential for processing large amounts of data to byte-size information
Statistics is also known as
Data Science
Machine Learning
Artificial Intelligence
So for today, we’re asking: what is Statistics?
Statistics is the science concerned with developing and studying methods for collecting, analyzing, interpreting and presenting empirical data.
Statistics is a mathematical body of science that pertains to the collection, analysis, interpretation or explanation, and presentation of data, or as a branch of mathematics.
Statistics is a branch of mathematics and a field of study that deals with the collection, analysis, interpretation, presentation, and organization of data.
Statistics is the science of collecting, analyzing, interpreting, and presenting data.
Objectively interpreting data to make meaningful inferences about our predictions.
Whatever the statistician says.
Gathering the narratives of individuals, groups, or society and telling a story about their past, present, or future. The numbers paint a picture worth many words.
Using numbers to try to explain behaviors and/or patterns in our world.
Statistics is the way to make sense of the natural world by taking data we collect to identify patterns between variables, and applying statistical theory to make sure we are taking the right approach to data collection and analysis. Also, assess patterns to see if they are reproducible and provide a logical explanation that makes biological sense.
Statistics is the study of data, patterns, and trends.
It is the study of variation and randomness!
Using mathematics, we model randomness to characterizes commonality and variation!
Using science, we systematically refine models to better fit randomness in data!
Using art, when it all eventually fails!
All models are wrong,
some are useful!
Statistics is both the development of mathematical models to be used in real-world data and the analysis of data using existing models.
Model observations that follow a new data generating process
Understand its properties
Develop new probability distributions
Known as Probability Theory
Researcher is a Probabilist or Mathematical Statistician
Model data with a known probability model
Account for sources of variation and bias
Account for violations of independence and randomness
Known as Statistician or Data Scientist
INFERENCE
Use our sample data to understand the larger population.
The data will tell us how the population generally behaves.
The data will guide us in the differences in units.
Data will tell us if there is a signal or just noise.
Are we seeing something different from what was expected? Or is it due to random chance?
We bring out the Monte Carlo methods!
There are two train of thoughts on how to interpret estimates and probability.
One approach is the Frequentist approach.
The other approach is the Bayesian approach.
Both sides hate each other.
A frequentist, in the context of statistics, is an individual who adheres to the frequentist interpretation of probability and statistical inference.
Meaning probability is obtained by the repetition of multiple experiments.
A Bayesians, in the context of statistics, is an individual who adheres to the Bayesian interpretation of probability and statistical inference.
Probability is obtained by likelihood of an event to occur, given data and prior knowledge.