Descriptive and Inferential Statistics

Descriptive and Inferential Statistics

Hopefully, you guessed right! The correct answer is: 

(b) 'Head length' and 'skull width' are positively associated. 

We will now turn our attention into the two main branches of statistics, Descriptive and Inferential. 

Descriptive Statistics is a method for summarising raw observations, e.g., the height of women in a room. 

 Example chart that measures the height of women in a room

| Image: example chart that measures the height of women in a room |  

Descriptive Statistics describes, presents, summarises and organises data via numerical calculations or graphs or tables. 

You can compare it to a written description or summary on the back of a book, which can be thought of as the raw data. 

Examples of descriptive statistics: 

  • Measures of centre of a data set: mean, median, mode. 
  • Measures of spread of a data set: range, variance, standard deviation. 
  • Measures of “connection” between two data sets: covariance, correlation. 

We are talking about Inferential Statistics when we are using small amounts of data to generalise, for example using the height of women in this room to ‘infer’ to the average height of women in UK. 

Inferential Statistics draws conclusions about all possible data on a subject (population) using only some of the data (sample). 

It is rarely possible to have access to and to use all data; the time and cost involved would be prohibitive. However, to base conclusions for all data, which is the population, on the basis of only some of the data, which is a sample, the sample has to be representative of the population. 


Testing the efficiency of the Covid-19 vaccines was not done on all people (the population) but only on some people (sample). It is only possible to draw conclusions for those parts of the populations that are represented in the samples. If the samples only contain people above 16 years of age, we cannot conclude anything for people younger than 16. 

Inferential statistics relies on representative samples. In the next lecture, let’s find out about the key sampling methods. 

When you’re ready, select Next to continue.


In this Course, we will find out about the concepts underpinning Statistics. 

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