Inferential Statistics

Inferential Statistics: Types of Inferential statistics and its importance by Muhammad Yousuf Ali

There are two main branch of statistics one is descriptive statistics and another is inferential statistics. Inferential statistics are a branch of statistics. It draws inferences or makes predictions based on research data. This article describes the following points about inferential statistics.

1. What is Inferential Statistics?

2. What are the types of Inferential Statistics?

3. What are the uses of inferential statistics?

4. What is the importance of inferential statistics?

What is Inferential Statistics ?

Inferential statistics is a branch of statistics that helps researchers to draw an inference or make predictions based on research data. It enables researchers to identify the patterns and trends about the large data or a specific population.

What are the types of Inferential Statistics?

Types of Inferential Statistics

Inferential statistics are divided into two categories:

1. Hypothesis testing.

2. Regression analysis.

1. Hypothesis testing

Hypothesis testing, a type of inferential statistics, involves subjecting research assumptions about a specific population parameter to examination. It’s used to determine the relationship between two or more variables.

Methods of Hypothesis Testing

  1. F-Test
    An F-test is a statistical test that compares the variances of two samples or the ratio of variances between multiple samples. It’s used in hypothesis testing to check whether the variances of two populations or two samples are equal.
  2. t-Test
    A t-test is a statistical method of hypothesis test. It determines if there is a statistically significant difference between the means of two groups.
  3. Z-Test
    A Z-test is a statistical hypothesis test that determines whether two population means are different. It’s based on the normal distribution and is used when the variances are known and the sample size is large.
  4. Chi-Square Test
    The chi-square statistic compares the size of any discrepancies between the expected results and the actual results, given the size of the sample and the number of variables in the relationship.
  5. ANOVA
    ANOVA stands for Analysis of Variance. It’s a statistical method that compares the means of multiple groups to analyze differences among group means in a sample.
  6. Wilcoxon signed-rank test
    The Wilcoxon signed-rank test is a non-parametric statistical test that compares two dependent samples. It can also be used to test the location of a population based on a sample of data.
  7. Mann-Whitney U test
    The Mann-Whitney U test is used to compare differences between two independent groups when the dependent variable is either ordinal or continuous, but not normally distributed.
  8. Kruskal-Wallis H test
    The Kruskal-Wallis H test (sometimes also called the “one-way ANOVA on ranks”) is a rank-based nonparametric test that can be used to determine if there are statistically significant differences between two or more groups of an independent variable on a continuous or ordinal dependent variable.
Types of Inferential Statistics
Hypothesis TestingRegression Analysis
t-TestLinear Regression
f-testNominal Regression
Z-testLogistic Regression
Chi-Square testOrdinal Regression
ANOVA  
Wilcoxon signed-rank test 
Mann-Whitney U test 
Kruskal-Wallis H test, etc. 

2. Regression Analysis

Regression analysis is part of inferential statistics, Regression analysis comprises a series of statistical procedures aimed at estimating the relationship between one dependent variable and one or more independent variables.

Types of Regression Analysis :-

Linear Regression

Linear regression is a statistical method that uses data analysis to predict the value of unknown data. It’s also known as simple regression or ordinary least squares (OLS).

Nominal Regression

Nominal logistic regression is a statistical modeling technique that examines the relationship between a set of predictors and a nominal response. A nominal response is a result that has three or more outcomes that are not in order.

Logistic Regression

Logistic regression is a statistical analysis method that uses mathematics to predict a binary outcome based on prior observations.

Ordinal Regression

Ordinal regression is a statistical technique that predicts the behavior of ordinal level dependent variables with a set of independent variables. It is also called ordinal classification.

Basically it is used to identify the modeling of different theoretical frameworks.

3. What are uses of inferential statistics?

There are three primary uses for inferential statistics:

Testing theories to make conclusions about populations.

Researchers can generalize a population by utilizing inferential statistics and a representative sample. It requires logical reasoning to reach conclusions.

4. What is the importance of inferential statistics?

  • Inferential statistics helpful to Testing theories and make conclusions about populations.
  • Inferential statistics uses analytical tools to determine what a sample’s data says about the whole population.
  • Inferential statistics include things like testing a hypothesis and looking at how things change over time.
  • Inferential statistics utilize sampling methods to identify samples that accurately represent the entire population.
  • Inferential statistics tests employs (Z test, the t-test, and linear regression) what is ongoing occurrence.
  •  Inferential statistics provide population estimations.

How to Cite this Article :

M.Y. Ali (2024). Inferential Statistics: Types of Inferential statistics and its importance. https://profileusuf.wordpress.com/inferential-statistics/