HYPOTHESIS
FORMULATION
Dr. Ajay Kumar Koli, PhD | SARA Institute of Data Science, India
Variable
A variable is a characteristic or attribute that can take on different values.
Independent Variables
The variable that is manipulated or controlled by the researcher to observe its effect on another variable.
- IV is also called as predictor variable.
Dependent Variables
The variable that is measured or observed to assess the impact of the independent variable.
- DV is also called as Effect or output or outcome variable.
Example
Example 1: Education Study
- Independent Variable: Teaching method (traditional vs. interactive).
- Dependent Variable: Student performance on a test.
Example
Example 2: Health Study
- Independent Variable: Amount of daily exercise (30 mins, 60 mins, 90 mins).
- Dependent Variable: Weight loss (in kilograms).
Example
Example 3: Marketing Study
- Independent Variable: Type of advertisement (video, banner, text).
- Dependent Variable: Number of clicks or purchases.
Practice
We’ll use data to demonstrate the relationship between independent and dependent variables. Examples are:
Direct Relationship
Inverse Relationship
No-Relation Example
Hypothesis
A hypothesis is a testable statement or prediction about the relationship between two or more variables.
Types of Hypotheses
Null Hypothesis (H0): The null hypothesis states that there is no effect, no difference, or no relationship between the variables being studied. It assumes that any observed differences are due to chance.
Alternative Hypothesis (Ha or H1): The alternative hypothesis suggests that there is an effect or relationship between the variables. It is the hypothesis that is supported if the null hypothesis is rejected.
Formulate Hypotheses
📚 Education Scenario: Investigating whether a new teaching method improves student test scores compared to the traditional method.
Null Hypothesis (H0):: The new teaching method has no effect on student test scores.
Alternative Hypothesis (Ha or H1): The new teaching method affects student test scores.
Null Hypothesis (H0):: The new teaching method does not improve student test scores.
Alternative Hypothesis (Ha or H1): The new teaching method improves student test scores.
Key Points
Non-Directional Hypotheses are used when you are testing for any effect, regardless of the direction (e.g., an increase or decrease).
Directional Hypotheses are used when you have a specific prediction about the direction of the effect (e.g., an improvement or reduction).
Formulate Hypotheses
💉 Medicine Scenario: Evaluating whether Drug A lowers blood pressure more effectively than a placebo.
Null Hypothesis (H0): Drug A has no effect on blood pressure compared to the placebo.
Alternative Hypothesis (Ha or H1): Drug A affects blood pressure compared to the placebo.
Null Hypothesis (H0): Drug A does not lower blood pressure more effectively than the placebo.
Alternative Hypothesis (Ha or H1): Drug A lowers blood pressure more effectively than the placebo.
Formulate Hypotheses
🌱 Environment Scenario: Determining whether an area’s air quality index (AQI) changes after implementing stricter pollution regulations.
Null Hypothesis (H0): Stricter pollution regulations have no effect on the AQI.
Alternative Hypothesis (Ha or H1): Stricter pollution regulations affect the AQI.
Null Hypothesis (H0): Stricter pollution regulations do not improve the AQI.
Alternative Hypothesis (Ha or H1): Stricter pollution regulations improve the AQI.
Formulate Hypotheses
💭 Psychology Scenario: Testing whether mindfulness training reduces stress levels among participants.
Null Hypothesis (H0): Mindfulness training has no effect on stress levels.
Alternative Hypothesis (Ha or H1): Mindfulness training affects stress levels.
Null Hypothesis (H0): Mindfulness training does not reduce stress levels.
Alternative Hypothesis (Ha or H1): Mindfulness training reduces stress levels.
Formulate Hypotheses
🧬 Biology Scenario: Investigating whether a specific fertilizer increases plant growth compared to no fertilizer.
Null Hypothesis (H0): The fertilizer has no effect on plant growth.
Alternative Hypothesis (Ha or H1): The fertilizer affects plant growth.
Null Hypothesis (H0): The fertilizer does not increase plant growth.
Alternative Hypothesis (Ha or H1): The fertilizer increases plant growth.
Formulate Hypotheses
🚀 Technology Scenario: Examining whether a new smartphone app decreases the average time users spend on their phones daily.
Null Hypothesis (H0): The app has no effect on the average time users spend on their phones.
Alternative Hypothesis (Ha or H1): The app affects the average time users spend on their phones.
Null Hypothesis (H0): The app does not decrease the average time users spend on their phones.
Alternative Hypothesis (Ha or H1): The app decreases the average time users spend on their phones.
Formulate Hypotheses
💰 Economics Scenario: Testing whether increasing the minimum wage influences unemployment rates.
Null Hypothesis (H0): Increasing the minimum wage has no effect on unemployment rates.
Alternative Hypothesis (Ha or H1): Increasing the minimum wage affects unemployment rates.
Null Hypothesis (H0): Increasing the minimum wage does not increase unemployment rates.
Alternative Hypothesis (Ha or H1): Increasing the minimum wage increases unemployment rates.
Key Differences
| Aspect | Directional Hypothesis | Non-Directional Hypothesis |
|---|---|---|
| Prediction | Specifies the direction of the effect (e.g., increase, decrease). | Predicts an effect but no specific direction. |
| Test Type | One-tailed test | Two-tailed test |
| Use Case | When prior research or theory suggests a specific outcome. | When unsure of the outcome’s direction. |
| Hypothesis Example | Ha: Group A scores are higher than Group B scores. | Ha: Group A scores are different from Group B scores. |
| Null Hypothesis (H0) | No effect in the predicted direction. | No effect or difference. |
