Have you seen officials coming from the survey department to your doorstep and inquiring about the number of people in the family, how many males and females, if there are senior citizens, if yes, how many, and what are their ages?
This data is collected from throughout the country, analysed, and then we get the information about the population of our country.
One instance is before every election; we have exit polls where every voter’s opinion is taken when they leave the polling booth. Depending on this data, they can predict the outcome of any election.
Another example is a report of numbers saying how many followers of each religion are there in a particular country.
This surveying, collecting data from various sources, analysing, presenting and interpreting is called Statistics.
The necessity to convert the vast volumes of data handy in many applied areas into usable information has caused theoretical and practical advances in statistics.
Statistical analysis is mainly used to collect and evaluate vast amounts of data. Statistics is a discipline of mathematics in which computations are performed on large amounts of data utilising charts, tables, graphs, etc. The information gathered for analysis here is referred to as measurements.
TYPES OF STATISTICS
To be more specific, Statistics can be divided into two types
- Sampling Statistics
- Descriptive Statistics
(c) Inferential Statistics
( i ) SAMPLING STATISTICS
The sampling technique describes how we obtain data. This is the most crucial aspect of statistics. Moreover, it ensures that we systematically collect our data to draw appropriate conclusions in the future. It also tells us when we have just enough data for future investigation. Remember that research can rapidly break apart even if you apply the greatest formulae and techniques if the data is not collected correctly.
For example, if a drug manufacturing company wants to investigate the adverse side effects of medicine on its population, it is tough to undertake a research study that includes everyone. In this situation, the researcher selects a sample of people from each sector of the population and then surveys them, providing preliminary input on the drug’s behaviour.
( ii) DESCRIPTIVE STATISTICS
Descriptive statistics is concerned with explaining or stressing essential features of our data. When people think about statistics, they frequently think of this field.
The tools in this section are for explaining the information you’ve gathered, and tasks like utiliSing a graph or computing an average sample fall under the category of descriptive statistics.
Descriptive statistics are used to give the sample greater meaning. Let’s take a closer look at this. You must first select the group that most interests you. You must first collect information about the group members before presenting the group’s attributes using summary statistics and graphs. There is no ambiguity with descriptive statistics since you describe the individuals or stuff you measure. You’re not attempting to deduce characteristics about a bigger population.
For example, if the teacher wants to find out the average marks scored in a particular subject in her class. We record all of the test scores. She can calculate the summary statistics and produce graphs. With the help of this graph, the mean(average), median, and mode can be calculated.
This data collected paints a reasonably accurate picture of this particular class. We do not doubt these numbers because we obtained the results for everyone in the class. However, we cannot apply these findings to a broader sample of pupils. This is where Inferential Statistics comes into the picture.
(iii) INFERENTIAL STATISTICS
Inferential Statistics is concerned with creating predictions based on obtained data. The idea here is to gather information, thoroughly evaluate it, and then draw conclusions about the broader picture.
This aspect of statistics may appear to be the most enigmatic, but in truth, it is one of the most powerful, allowing us to gain more information from the data we have already gathered.
Inferential statistics are used to generate generalisations about a population based on data from samples. For example, you may stand at a mall and poll 100 individuals to see if they prefer shopping at a particular store.
DIFFERENCE BETWEEN DESCRIPTIVE AND INFERENTIAL STATISTICS
|Sl.no||Descriptive Statistics||Inferential Statistics|
|1||Data may be handled and compiled using numbers and graphs.||To gain insights from the population, sample data must be used.|
|2||Explains essence of sample or population||Make an inference or extract a conclusion|
|3||This method is chosen when the data set is small||This method is chosen when population data is extensive|
|4||It describes a situation||Describes how likely a certain event is to occur|
|5||Charts, numbers and Graphs are required|
To compute the result
|A probability measure is used to achieve the result.|
DIFFERENT STAGES OF STATISTICS
Collecting the Data: Statistical data are collected through various approaches.
Statistical Tool Used: Census or Sample method.
Proper Organization of the Data:: The above-collected data is well organised in some schematic way.
Statistical Tool Used: Array of Data and Tally bar.
Display of data: The well-organized data are displayed in graphs, charts, tables etc.
Statistical Tool Used: Table, graphs and schematic diagram.
An analysis of the data: We analyse the data using averages of percentages in the fourth stage.
Statistical Tool Used: Percentage and Average
Interpretation of the collected data: In this last stage, we interpret the data to conclude it is reached.
Statistical Tool Used: Percentage, average, degree of relationship between variables.
APPLICATION OF STATISTICS IN DAILY LIFE
Let us now see a few Applications of Statistics in Our Daily Life :
(1) In the Medical Department: Statistics are utilised to support almost all medical research. Statistics help doctors in determining the mental development of a child and how efficient the therapies are.
2) Weather Predictions
Mathematics and statistics play a critical role in observation, analysis, and prediction. By comparing weather observations, forecast models can be developed.
3) Testing the quality of the product
Every day, a company manufactures hundreds of products and ensures that only the best things are sold. Companies can’t test every product. As a result, the organisation employs quality testing with the support of statistics.
4) Stock Market
The stock market also uses a computerised model for stock analysis. Stock analysts use statistics ideas to obtain knowledge about the economy.
5) Fashion & Apparel
Retailers use statistics to keep track of everything they sell and to maintain track of their inventory. Leading retailers across the world utilise analytics to determine which items ship to which stores and when.
Since you now understand what statistics are and the many types of statistics, you should understand why they are significant in everyday life. We would not be able to have any information around us if statistics did not exist.
Frequently Asked Questions(FAQ)
1. Where is Statistics mainly used?
Statistics are commonly employed in a wide range of applications and professions. Statistics are created whenever data is collected and examined. This can include everything from government organisations to academic research to financial analysis.
2. What are the types of Statistics?
Different types of Statistics are Descriptive Statistics and Inferential Statistics.
3. What are the uses of Statistics in Biology?
Statistics are used in biology for experimental data. Statistics provide critical information on many illness incidences, birth and mortality rates, the extent to which an infection spreads and recovers, the population at a certain time, etc.