Data in Statistics
Binny Paulose
Sr. Manager OEM Alliances & Partnerships -Google Cloud, VMware & Red Hat
The amount of data created each year is growing faster than ever before. By end 2020, every human on the planet will be creating 1.7 megabytes of information each second! In only a year, the accumulated world data will grow to 44 zettabytes (that's 44 trillion gigabytes)
So, what is Data?
Data is a collection of facts, such as numbers, words, measurements, observations or just descriptions of things from which conclusions can be drawn.
Data can be classified into Quantitative and Qualitative.
Quantitative data or numeric data deals with numbers and things you can measure. Examples include height, width, length, temperature, humidity, prices, area and volume.
Qualitative data deals with characteristics and descriptions that can't be easily measured , but can be observed. Examples include smell, taste, textures, attractiveness and colour.
Quantitative data can be further classified into continuous and discrete.
Continuous data could be divided and reduced to finer and finer levels. For example, you can measure the weight of an object at progressively more precise scales-metric ton, kilogram, gram, milligrams. So, weight is a continuous data.
Discrete data is a count that can't be made more precise. For instance, the number of boys in class. You can have either 20 or 21 boys. You can’t count 21.5 number of boys.
Quantitative data can be further classified into Binomial, Nominal and Ordinal.
Binomial Data: The binomial data has two possible outcomes. For example, a coin toss has only two possible outcomes: heads or tails .Taking a test could have two possible outcomes: pass or fail.
Nominal Data: The nominal data is used to label variables without being ordered or measured. Examples include country, gender, race, hair colour.
Ordinal Data: Ordinal data is where the variables have natural ordered categories and but the distances between the categories is not known.For Example, survey responses 1,2,3 to 5,satisfaction rating (“extremely dislike”, “dislike”, “neutral”, “like”, “extremely like”).