**Types of Hypothesis **

The hypothesis is the tested relationship between two or more two variables. The structure or format of hypothesis formulation depends upon the nature of the relationship predicted by the researcher.

- Descriptive/ Univariate Hypothesis
- Explanatory Hypothesis/Causal /Bivariate Hypothesis
- Directional Hypothesis
- Non-Directional Hypothesis
- Null Hypothesis
- Alternative Hypothesis/Maintained hypothesis/Research hypothesis

**Descriptive/ Univariate Hypothesis**

The descriptive hypothesis is a type of hypothesis that is based on data. The **descriptive hypothesis** contains only one variable so it is called a univariate hypothesis. The descriptive hypothesis typically states the existence, size, form or distribution of some variable. It describes the situation or collects the data, the same as it has happened without adding our own assumptions or judgments.

**Characteristics of Descriptive/ Univariate Hypothesis**

i. Single Variable Focus:

ii. Descriptive Nature:

iii. Prior Knowledge:

iv. Objective and Testable:

v. Clear Direction:

vi. Generalizable Findings:

vii. Statistical Analysis:

viii. Research Limitations and Further Research:

**Designing a Study to Test a Descriptive Hypothesis**

i. Define the Variable:

ii. Determine the Population:

iii. Sampling Strategy:

iv. Data Collection:

v. Analysis of Data:

vi. Interpretation of Results:

vii. Limitations and Further Research:

**Applications of Descriptive/Univariate Hypothesis**

i. Social Sciences:

ii. Economics:

iii. Health Sciences:

iv. Education:

**Explanatory/ Causal /Bivariate Hypothesis**

An explanatory hypothesis is a type of hypothesis that is used to test the cause-and-effect relationship between two or more two variables. The independent variable is manipulated to cause-effect on the dependent variable and the dependent variable is measured to examine the effect created by the independent variable.

**Example of Explanatory/ Causal /Bivariate Hypothesis,**

An increase in age would lead to a decrease in organizational commitment.

**Components of an Explanatory Hypothesis**

i. Variables:

ii. Cause and Effect Relationship:

iii. Prediction:

iv. Testability:

**Challenges of Explanatory Hypothesis**

i. Relationship Complexity:

ii. Causal Inference:

iii. Third-Variable Problem:

iv. Causality Reversed:

v. Sample Selection Bias:

vi. Generalizability:

vii. Ethical Considerations:

viii. Unobserved Variables and Mediation:

**Directional Hypothesis**

In terms of positive, negative, and zero direction of the relationship between variables is indicated by this hypothesis. In the **directional hypothesis**, the direction of the relationship has been specified.

**Example of Directional hypothesis,**

Women are more motivated than men.

**Components of a Directional Hypothesis**

i. Variables:

a. Independent Variable:

b. Dependent Variable:

ii. Predicted Direction:

a. Positive Direction:

b. Negative Direction:

iii. Rationale:

**Importance of Directional Hypothesis**

i. Guiding Research Efforts:

ii. Interpreting Results:

iii. Research Contribution:

iv. Statistical Analysis:

**Challenges of Directional Hypothesis**

i. Relationship Complexity:

ii. Causal Inference:

iii. Non-Directional Relationships:

iv. Sample Selection Bias:

v. Operationalization and Measurement:

vi. Statistics and Sample Size:

vii. Publication Bias:

**Non-Directional Hypothesis**

A non-Directional Hypothesis is a type of hypothesis in which the direction of the relationship between the variable is not specified. The relationship between the variable may be very strong, but whether it is positive or negative has not been specified. These hypotheses are formulated in cases where previous studies do not exist or indicate conflicting or contradictory findings.

**Example of a Non-Directional hypothesis,**

There is a difference between the work attitudes of industrial and agricultural workers.

**Components of Non-Directional Hypothesis**

i. Research Question:

ii. Variables:

iii. Population:

iv. Null Hypothesis (H0):

v. Alternative Hypothesis (H1 or Ha):

vi. Statistical Test:

**Importance of Non-Directional Hypothesis**

i. Openness to all Possible Outcomes:

ii. Comprehensive Investigation:

iii. Data Analysis Flexibility:

iv. Managing Confirmation Bias:

v. Adaptability to Different Research Questions:

vi. Statistical Testing:

**Challenges of Non-Directional Hypothesis**

i. Increased Sample Size Requirements:

ii. Reduced Statistical Power:

iii. Less Precision in Research Question:

iv. Interpretation Challenges:

v. Potential Overlook of Meaningful Effects:

vi. Researcher Bias in Reporting Results:

**Null Hypothesis**

Null Hypothesis is a type of hypothesis formulated by the researcher for testing the hypothesis. The ‘null’ often refers to the common view of something, while the alternative hypothesis is what the researcher really thinks is the cause of a phenomenon.

A **null hypothesis** is a hypothesis that says there is no statistical significance between the two variables. It is usually the hypothesis a researcher or experimenter will try to disprove or discredit.

**Components of Null Hypothesis**

i. Parameters of the Population:

ii. Equality or Lack of Effect:

iii. Mathematical Statement:

iv. Testable Statement:

v. Directionality:

vi. Alternative Hypothesis:

**Importance of Null Hypothesis**

i. Baseline Comparison:

ii. Testable Hypothesis:

iii. Counterfactual Scenario:

iv. Statistical Significance:

v. Decision-Making:

vi. Reproducibility and Generalizability:

vii. Refining Research Questions:

**Challenges of Null Hypothesis**

i. Arbitrary Choice:

ii. Oversimplification:

iii. Sample Size Consideration:

iv. P-value Misinterpretation:

v. Limited Scope:

vi. Publication Bias:

vii. Contextual Consideration:

**Alternative Hypothesis/Maintained Hypothesis/Research hypothesis**

Alternative Hypothesis is a type of hypothesis that states there is a statistically significant relationship between two variables.

**Types of Hypothesis Quiz (MCQs)**

**What is the null hypothesis?**

a. A statement about a population parameter that is assumed to be true until proven otherwise

b. A statement about a population parameter that is assumed to be false until proven otherwise

c. A statement about a sample statistic that is assumed to be true until proven otherwise

d. A statement about a sample statistic that is assumed to be false until proven otherwise

Answer: a

**What is the alternative hypothesis?**

a. A statement about a population parameter that is assumed to be true until proven otherwise

b. A statement about a population parameter that is assumed to be false until proven otherwise

c. A statement about a sample statistic that is assumed to be true until proven otherwise

d. A statement about a sample statistic that is assumed to be false until proven otherwise

Answer: b

**What type of hypothesis test is used when the population standard deviation is unknown?**

a. Z-test

b. T-test

c. Chi-squared test

d. F-test

Answer: b

**What type of hypothesis test is used when comparing two means?**

a. Z-test

b. T-test

c. Chi-squared test

d. F-test

Answer: b

**What type of hypothesis test is used when comparing two proportions?**

a. Z-test

b. T-test

c. Chi-squared test

d. F-test

Answer: a

**What type of hypothesis test is used when comparing multiple means?**

a. Z-test

b. T-test

c. ANOVA

d. F-test

Answer: c

**What type of hypothesis test is used when comparing multiple variances?**

a. Z-test

b. T-test

c. ANOVA

d. F-test

Answer: d

**What type of hypothesis test is used when testing for independence between two categorical variables?**

a. Z-test

b. T-test

c. Chi-squared test

d. F-test

Answer: c

**What type of hypothesis test is used when testing for goodness of fit?**

a. Z-test

b. T-test

c. Chi-squared test

d. F-test

Answer: c

**What is the difference between a one-tailed and two-tailed hypothesis test?**

a. One-tailed tests only test for a difference in one direction, while two-tailed tests test for a difference in either direction

b. One-tailed tests use a different statistical method than two-tailed tests

c. One-tailed tests are more accurate than two-tailed tests

d. Two-tailed tests are more accurate than one-tailed tests

Answer: a

**How is the level of significance determined in a hypothesis test?**

a. By the size of the sample

b. By the size of the population

c. By the desired level of confidence in the results

d. By the size of the margin of error

Answer: c

**What is a Type I error in a hypothesis test?**

a. Rejecting the null hypothesis when it is true

b. Accepting the null hypothesis when it is true

c. Rejecting the alternative hypothesis when it is true

d. Accepting the alternative hypothesis when it is true

Answer: a

**What is a Type II error in a hypothesis test?**

a. Rejecting the null hypothesis when it is true

b. Accepting the null hypothesis when it is false

c. Rejecting the alternative hypothesis when it is true

d. Accepting the alternative hypothesis when it is false

Answer: b

**What is a p-value in a hypothesis test?**

a. The probability of obtaining a sample statistic as extreme or more extreme than the one observed, assuming the null hypothesis is true

b. The probability of obtaining a sample statistic as extreme or more extreme than the one observed, assuming the alternative hypothesis is true

c. The probability of accepting the null hypothesis when it is true

d. The probability of rejecting the null hypothesis when it is true

Answer: a

**What is the power of a hypothesis test?**

a. The probability of correctly rejecting the null hypothesis when it is false

b. The probability of correctly accepting the null hypothesis when it is true

c. The probability of correctly rejecting the alternative hypothesis when it is true

d. The probability of correctly accepting the alternative hypothesis when it is false

Answer: a

**What is a chi-squared test?**

a. A test used to compare two means

b. A test used to compare two proportions

c. A test used to compare multiple means

d. A test used to compare two or more categorical variables for independence or goodness of fit

Answer: d

**What is an ANOVA test?**

a. A test used to compare two means

b. A test used to compare two proportions

c. A test used to compare multiple means

d. A test used to compare two or more categorical variables for independence or goodness of fit

Answer: c

**What is a two-sample t-test?**

a. A test used to compare the means of two independent samples

b. A test used to compare the means of two correlated samples

c. A test used to compare the variances of two independent samples

d. A test used to compare the variances of two correlated samples

Answer: a

**What is a paired t-test?**

a. A test used to compare the means of two independent samples

b. A test used to compare the means of two correlated samples

c. A test used to compare the variances of two independent samples

d. A test used to compare the variances of two correlated samples

Answer: b

**What is a z-test?**

a. A test used to compare the means of two independent samples

b. A test used to compare the means of two correlated samples

c. A test used to compare the proportions of two independent samples

d. A test used to compare the proportions of two correlated samples

Answer: c

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Hello !

I am still not getting Descriptive Hypothesis. Can you please provide some examples with explanation.

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