A Detailed Guide on The Branches of Statistics

branches-of-statistics

To learn statistics holistically, students should know two branches of statistics: descriptive and inferential statistics. It is essential to learn and understand the whole idea of statistical analysis.

These branches of statistics follow a particular scientific approach, which makes them equally important to every statistical student. In this blog, we will discuss the branches of statistics. Before we get started, let’s have a quick look at what statistics is.

What is Statistics?

Statistics is the main branch of mathematics. Used to perform different operations, i.e., Data collection, organization, analysis, and so on. In other words, statistics is a form of mathematical analysis that uses quantitative models to give a set of experimental data or studies of real life. Statistics examine the methodology for collecting, reviewing, analyzing, and making data conclusions. Some statistical measures include the following:

  • Mean: It is an important concept in mathematics and statistics. The mean is an average and the most common value in the collection of numbers.
  • Regression analysis is an effective statistical technique for investigating the connection between two or more relevant variables.
  • Skewness: Skewness is the statistical term for the degree of asymmetry seen in a probability distribution. Distributions can display right (positive) skewness or left (negative) skewness to differing degrees. A normal distribution (bell curve) presents zero skewness.
  • Kurtosis: It is a quantification of the total weight of a distribution’s tails in relation to the distribution’s center.
  • Variance: It estimates the variability from the mean or average. 
  • Analysis of variance: The method of statistics that separates the variance data into several components used for additional tests.
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Branches Of Statistics

Descriptive Statistics

Descriptive statistics is the first part of statistics that deals with data collection. People think it is too easy, but it is not that easy. Statisticians need to be aware of the design and experiments. They also need to select the correct focus group and avoid biases. On the contrary, Descriptive statistics are used to do various kinds of analysis in different studies.

Example of Descriptive StatisticsThe average score of the college students in the math test.The average age of the people who voted for the winning candidate in the last election.The average length of the statistics book.

Descriptive statistics have two parts;

  • Central tendency measures
  • Variability measures

The tendency and variability measures use tables, general discussions, and charts to help users understand the analyzed data.

Measures of Central Tendency

Central tendency measures specifically help statisticians evaluate the distribution center of values. These tendency measures are:

Mean

Mean is a conventional method for describing central tendency. Typically, it calculates the average of values, counts all values, and then divides them by the number of available values.

Formula of Mean

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m= Sum of the terms/numbers of terms

For Example: Calculate the mean of the following data:10, 10, 40, 50, 20Solution: m= Sum of the terms/numbers of terms10+10+40+50+20/5130/5 = 26Thus, mean=26

Median

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It is the result in the middle of a set of values. Editing the results in numerical journals and locating the result in the center of the distributed sample is an easy way to calculate the median.

Formula of Median

there are two formulas for the median;

  • When n is odd,

(n+1 / 2)th observation

  • When n is even,

median=(n/2)th + (n/2+ 1)th observation / 2

For Example 1: Find the median of 4, 1, and 7.Solution: As the given are odd numbers of observations, then we apply the formula,median= (n+1)/2Thus, the median is3 + 1 / 2 = 4 / 2 = 2Median = 2
For Example 2: Find the median of the data 2, 4, 6, 8, 10, 12.Solution: As the given numbers are even of observation, then we use the formula (n is even),We will pick the middle numbers 6 and 8. Thus, the median will be6+8/214/2 median= 7

Mode

The mode is the frequently occurring value in the given data set.

For Example: Find the mode of the given data,4, 2, 4, 3, 2, 2Solution: Arrange the numbers 2, 2, 2, 3, 4, 4Now you can see that the number 2 is found three times. Thus, the mode of the given data is 2.

Measures of Variability

The variability measure helps statisticians analyze the distribution spreading from a specific data set. Variables of variability include quartiles, ranges, variances, and standard deviations.

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Inferential Statistics

Inference statistics are techniques that enable statisticians to use the information collected from the sample to conclude, bring decisions, or predict a defined population.

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Inference statistics often speak in terms of probability by using descriptive statistics. A statistician uses these techniques for data analysis, drafting, and making conclusions from limited information. That is obtained by taking samples and testing their reliability.

Most future predictions and generalizations based on a smaller specimen population study are within the scope of inference statistics. Besides, most social science experiments involve studying a small sample population that helps determine the community’s behavior.

When designing a real experiment, the researcher can draw relevant conclusions to his study. However, he should be cautious not to draw wrong conclusions or be biased.

Example of Inferential StatisticsSuppose you want to get an idea about the percentage of the people who love shopping at FILA. We take the sample of the population and find the proportion of individuals who love the FILA brand. With the assistance of probability, this sample proportion allows us to make a few assumptions about the population proportion. This study belongs to inferential statistics.

Different types of inferential statistics include:

  • Regression analysis: It is a collection of statistical techniques for calculating the correlations between one or more independent variables and a dependent variable. It includes several variations, like linear, multiple linear, and nonlinear. The most well-known models are simple linear and multiple linear.
  • Analysis of variance (ANOVA): One statistical technique is ANOVA, which distributes observed variance data into various components. A one-way ANOVA is applied to three or more data groups to gain information about the relationship between the dependent and independent variables.
  • Analysis of covariance (ANCOVA): It is used to test categorical variables’ main and interaction effects on constant dependent variables and keep control for the impact of selected other constant variables. The control variables are known as covariates. 
  • Statistical significance (t-test): This is applied to establish the presence of a substantial difference in the means between two groups in some aspects. A t-test measures the t-statistic, the t-distribution values and the degree of freedom to determine statistical significance.
  • Correlation analysis is a method of statistical analysis by which one determines a relation between two variables or two sets of variables and the possible quality of their association.

Conclusion

Now, it will be clear as to what branches of statistics are there or exist, or you may be in a better position to say so. As usual, they reminded us that we are to focus on the topic and find out more details about it. Besides, this is just a beginner guide on the branches of statistics if you are a statistics student. And if you need statistics homework help, then we are here to help you.

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Frequently Asked Questions

What are the branches of demography in statistics?

Demography refers to the study of population, and it focuses on five aspects, which are as follows;
Size
Geographical Distribution
Composition
The components of change (birth, death, or migration)
The determinants and consequences of population change.

What is the difference between the two branches of statistics?

Statistics is divided into two major parts: descriptive and inferential statistics. Descriptive statistics represent what is going on in a population or data set. On the contrary, inferential statistics enables the statisticians to take findings from a sample group and generalize them to a larger population.