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Understanding the Main Types of Statistics in Maths

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Descriptive and Inferential Statistics Definition Formulas and Examples

Any raw Data, when collected and organized in the form of numerical or tables, is known as Statistics. Statistics is also the mathematical study of the probability of events occurring based on known quantitative Data or a Collection of Data.

Statistics attempts to infer the properties of a large Collection of Data from inspection of a sample of the Collection thereby allowing educated guesses to be made with a minimum of expense. There are generally 3 kinds of averages commonly used in Statistics. They are: (i) Mean, (ii) Median, and (iii) Mode. 

Statistics is the study of Data Collection, Analysis, Interpretation, Presentation, and organizing in a specific way. Mathematical methods used for different analytics include mathematical Analysis, linear algebra, stochastic Analysis, the theory of measure-theoretical probability, and differential equations. Collecting, classifying, organizing, and displaying numerical Data is associated with Statistics. This helps one to grasp different outcomes from it and foresee several possibilities of various events. Statistics discuss information, observations, and Data in the form of numerical Data. We are able to find different indicators of central tendencies and the divergence of various values from the center with the help of Statistics. 

The ability to analyze and interpret statistical Data is a vital skill for researchers and professionals from a wide variety of disciplines. You may need to make decisions on the basis of statistical Data, interpret statistical Data in research papers, do your own research, and interpret the Data.


Types of Statistics

There are two kinds of Statistics, which are descriptive Statistics and inferential Statistics. In descriptive Statistics, the Data or Collection Data are described in a summarized way, whereas in inferential Statistics, we make use of it in order to explain the descriptive kind. Both of them are used on a large scale. Also, there is another kind of Statistics where descriptive transitions into inferential Statistics.

Statistics is mainly divided into the following two categories. 

  1. Descriptive Statistics

  2. Inferential Statistics


Descriptive Statistics

In the descriptive Statistics, the Data is described in a summarized way. The summarization is done from the sample of the population using different parameters like Mean or standard deviation. Descriptive Statistics are a way of using charts, graphs, and summary measures to organize, represent, and explain a set of Data. 

  • Data is typically arranged and displayed in tables or graphs summarizing details such as histograms, pie charts, bars or scatter plots.

  • Descriptive Statistics are just descriptive and thus do not require normalization beyond the Data collected.


Inferential Statistics

In the Inferential Statistics, we try to interpret the Meaning of descriptive Statistics. After the Data has been collected, analyzed, and summarised we use Inferential Statistics to describe the Meaning of the collected Data. 

  • Inferential Statistics use the probability principle to assess whether trends contained in the research sample can be generalized to the larger population from which the sample originally comes.

  • Inferential Statistics are intended to test hypotheses and investigate relationships between variables and can be used to make population predictions.

  • Inferential Statistics are used to draw conclusions and inferences, i.e., to make valid generalizations from samples.


Example

In a class, the Data is the set of marks obtained by 50 students. Now when we take out the Data average, the result is the average of 50 students’ marks. If the average marks obtained by 50 students are 88 out of 100, on the basis of the outcome, we will draw a conclusion. 


Mean, Median and Mode in Statistics

Mean: Mean is considered the arithmetic average of a Data set that is found by adding the numbers in a set and dividing by the number of observations in the Data set. 

Median: The middle number in the Data set while listed in either ascending or descending order is the Median. 

Mode: The number that occurs the most in a Data set and ranges between the highest and lowest value is the Mode. 

For n number of observations, we have

Mean = \[\overline {x} = \frac{\sum x}{n} = \sum {x}{n} \]

Median = \[ \frac{\left[ \frac {n}{2} + 1 \right ]^{th} term}{2}\] if n is odd.

Median = \[\frac {\left[ \frac {n}{2} \right ]^{th} term +  \left[ \frac {n}{2} + 1 \right ]^{th} term }{2}\] if n is even.

Mode = The value which occurs most frequently


Measures of Dispersion in Statistics

The measures of central tendency do not suffice to describe the complete information about a given Data. Therefore, the variability is described by a value called the measure of dispersion. 

The different measures of dispersion include:

  1. The range in Statistics is calculated as the difference between the maximum value and the minimum value of the Data points.

  2. The quartile deviation that measures the absolute measure of dispersion. The Data points are divided into 3 quarters. Find the Median of the Data points. The Median of the Data points to the left of this Median is said to be the upper quartile and the Median of the Data points to the right of this Median is said to be the lower quartile. Upper quartile - lower quartile is the interquartile range. Half of this is the quartile deviation.

  3. The Mean deviation is the statistical measure to determine the average of the absolute difference between the items in a distribution and the Mean or Median of that series.

  4. The standard deviation is the measure of the amount of variation of a set of values.


Solved Example

1. What is the probability of getting two tails and one head, when 3 coins are tossed at a time?

(A) 15

(B) 3/8

(C) 14

(D) 17

Solution:

Step 1: Number of possible outcomes when one coin is tossed = 2. Outcomes are HHH and TTT. 

Step 2: The possible outcomes, when 3 coins are tossed are {TTT, THT, TTH, THH, HHT, HTH, HTT, HHH}. 

Step 3: Number of favorable outcomes = 3. Favorable outcomes are THT, TTH, HTT. 

Step 4: Substitute.

Step 5: So, the probability of getting two tails and one head is \[\frac{3}{8}\].

Correct answer: (B) \[\frac{3}{8}\].

 

Stages of Statistics

  1. Collection of Data:

This is the first step of statistical Analysis where we collect the Data using different methods depending upon the case.

  1. Organizing the Collected Data: 

In the next step, we organize the collected Data in a Meaningful manner. All the Data is made easier to understand.  

  1. Presentation of Data: 

In the third step we simplify the Data. These Data are presented in the form of tables, graphs, and diagrams.

  1. Analysis of the Data: 

Analysis is required to get the right results. It is often carried out using measures of central tendencies, measures of dispersion, correlation, regression, and interpolation.

  1. Interpretation of Data: 

In this last stage, conclusions are enacted. Use of comparisons is made. On this basis, forecasting is made.


Uses of Statistics

  • Statistics helps to obtain appropriate quantitative Data. 

  • Statistics helps to present complex Data for the simple and consistent Interpretation of the Data in a suitable tabular, diagrammatic, and graphic form.

  • Statistics help to explain the nature and pattern of variability through quantitative observations of a phenomenon.

  • Statistics help to depict the Data in tabular form, or in a graphical form in order to understand it properly. 


Applications of Statistics

  • Statistics is used in Machine Learning and Data Mining.

  • Statistics is used in Mathematics.

  • Statistics is used in Economics. 

FAQs on Understanding the Main Types of Statistics in Maths

1. What are the types of statistics?

The two main types of statistics are Descriptive Statistics and Inferential Statistics.

  • Descriptive statistics summarize and organize data using measures like mean, median, and standard deviation.
  • Inferential statistics use sample data to make predictions or generalizations about a population.
These two branches form the foundation of statistical analysis in mathematics.

2. What is descriptive statistics?

Descriptive statistics is the branch of statistics that summarizes and presents data in a meaningful way.

  • It uses measures of central tendency such as mean, median, and mode.
  • It includes measures of dispersion like range, variance, and standard deviation.
  • Data is often shown using tables, bar graphs, histograms, and pie charts.
It does not make predictions; it only describes the given dataset.

3. What is inferential statistics?

Inferential statistics is the branch of statistics that makes predictions or draws conclusions about a population based on sample data.

  • It uses techniques like hypothesis testing.
  • It calculates confidence intervals.
  • It applies probability theory to estimate population parameters.
For example, using a sample mean to estimate the population mean is an application of inferential statistics.

4. What is the difference between descriptive and inferential statistics?

The main difference is that descriptive statistics summarize data, while inferential statistics make predictions about a population.

  • Descriptive statistics deal only with the collected dataset.
  • Inferential statistics use a sample to draw conclusions about a larger population.
  • Descriptive examples: mean, median, graphs.
  • Inferential examples: hypothesis testing, confidence intervals.
Descriptive explains “what the data shows,” while inferential answers “what it means for the population.”

5. What are the main measures used in descriptive statistics?

The main measures in descriptive statistics are measures of central tendency and measures of dispersion.

  • Mean: Average value, calculated as \( \text{Mean} = \frac{\sum x}{n} \).
  • Median: Middle value in ordered data.
  • Mode: Most frequent value.
  • Range: Difference between maximum and minimum.
  • Standard deviation: Measures data spread.
These measures help summarize and understand the characteristics of a dataset.

6. Can you give an example of descriptive and inferential statistics?

Descriptive statistics summarize data, while inferential statistics make predictions from sample data.

  • Descriptive example: The average score of 30 students in a class is 75.
  • Inferential example: Using that class average to estimate the average score of all students in the school.
The first describes known data; the second generalizes to a larger group.

7. What is the role of probability in inferential statistics?

Probability provides the mathematical foundation for making predictions in inferential statistics.

  • It helps calculate the likelihood of outcomes.
  • It is used in hypothesis testing to determine statistical significance.
  • It supports the construction of confidence intervals.
Without probability theory, drawing reliable conclusions about a population would not be possible.

8. Why are there two types of statistics?

There are two types of statistics because data analysis involves both summarizing data and making predictions.

  • Descriptive statistics organize and present collected data clearly.
  • Inferential statistics extend findings from a sample to a population.
Together, they allow statisticians to both understand data and make informed decisions based on it.

9. What is hypothesis testing in inferential statistics?

Hypothesis testing is a method in inferential statistics used to test assumptions about a population parameter.

  • Step 1: State the null hypothesis (H₀) and alternative hypothesis (H₁).
  • Step 2: Choose a significance level (e.g., 0.05).
  • Step 3: Calculate a test statistic and p-value.
  • Step 4: Reject or fail to reject H₀.
It helps determine whether observed results are statistically significant.

10. How are statistics used in real life?

Statistics are used in real life to analyze data, make decisions, and predict outcomes.

  • In business: analyzing sales trends using descriptive statistics.
  • In healthcare: testing new treatments with inferential statistics.
  • In education: evaluating student performance using averages and standard deviation.
Both types of statistics help transform raw data into meaningful insights.