2. Python for Data Science: 8 Concepts You May Have Forgotten
Importance of Descriptive Statistics
What is Descriptive Statistics?
The discipline of quantitatively summarising the key elements of a collection of information, or the quantitative description itself, is known as descriptive statistics. Descriptive statistics are highly essential since it would be difficult to visualise what the data was indicating if we simply presented it as raw data, especially if there was a lot of it. Descriptive statistics help us to portray the data in a more meaningful form, allowing for easier understanding of the data.
When we employ descriptive statistics, we may use a combination of tabular description (i.e., tables), graphical description (i.e., graphs and charts), and statistical commentary to summarise our set of data (i.e., a discussion of the results).
Types of Descriptive Statistics
There are two types of statistics that are commonly used to describe data:
- Measures of central tendency - Measurements of central tendency are techniques of describing the centre of a frequency distribution for a set of data (consider Mean, Mode and Median). The frequency distribution in this situation is just the distribution and pattern of marks earned by the 100 students, from lowest to highest.
- Measures of spread - Measurements of spread are techniques of summarising a set of data by stating how evenly distributed the scores are. For example, our 100 students' average score may be 65 out of 100. Unfortunately, not every student will have received 65 points. Rather, their scores will be distributed evenly. Some will be lower, while others will be higher. Spread measures allow us to describe how evenly distributed these scores are.
Importance of Descriptive Statistics
The data is summarised and explained in descriptive statistics. The summarization is performed from a population sample using several metrics such as mean or standard deviation. Descriptive statistics are a method of organising, representing, and explaining a collection of data by utilising charts, graphs, and summary measurements. Below are a few important points why descriptive statistics is so crucial.
- Typically, data is organised and shown in tables or graphs that summarise features, such as histograms, pie charts, bars, or scatter plots. Descriptive statistics are naturally descriptive in nature and generally don't need to be normalised beyond the data that was obtained.
- Descriptive statistics are critical since representing raw data would make it more difficult to interpret. As a result, this descriptive statistic allows us to display the data in a more useful manner.
- Moreover, in order to collect data for your statistical study, you must first identify the population, which might be a group of people or items, depending on the subject of your descriptive research. Therefore, here, too, descriptive statistics can be utilised.
- To convey descriptive statistics, analysts frequently utilise charts and graphs. Descriptive statistics would be obtained if you stood outside a movie theatre and asked 50 members of the audience if they enjoyed the film they saw, then plotted your findings on a pie chart. In this case, descriptive statistics count the number of yes and no responses and illustrate how many people in this particular theatre enjoyed or hated the film. If you tried to draw any additional inferences, you'd be entering the inferential statistics area (Inferential statistics uses a random sample of data from a subset of the population to describe and infer the entire population).
- Lastly, political polling is classified as a descriptive statistic if it only presents tangible data (respondent responses) without reaching any judgements.
Conclusion
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