In scientific research, especially in biology, medicine, and statistics, we often need to test assumptions and check whether our findings are due to chance or real effects. This is where the concepts of null hypothesis (H₀) and alternative hypothesis (H₁ or Ha) become important.
Both are central to hypothesis testing, a statistical method used to draw conclusions from data. A hypothesis provides a statement or assumption about a population or process, and researchers test it using samples of data.
1. What is a Hypothesis?
A hypothesis is a tentative assumption or prediction about the relationship between two or more variables. It is not yet proven but can be tested through experiments, observations, or statistical analysis.
- In biology, a hypothesis might be:
“Fertilizer X increases the growth rate of tomato plants compared to plants without fertilizer.”
To test this, researchers set up null and alternative hypotheses.
2. Definition of Null Hypothesis (H₀)
The null hypothesis is a statement that assumes no relationship or no significant difference between two variables or groups. It suggests that any observed effect is due to chance.
- Symbol: H₀ (read as “H-null” or “H-zero”)
- Nature: Conservative assumption – assumes nothing new is happening.
- Mathematical Representation: Always contains an equals sign (=).
Example in Biology:
- H₀: “There is no difference in the average height of plants grown in sunlight and those grown in shade.”
- H₀: “A new drug has no effect on reducing blood pressure compared to a placebo.”
3. Definition of Alternative Hypothesis (H₁ or Ha)
The alternative hypothesis is the opposite of the null hypothesis. It states that there is a significant relationship or difference between the groups or variables being studied.
- Symbol: H₁ or Ha
- Nature: Progressive assumption – suggests something new or different is happening.
- Mathematical Representation: Uses ≠, >, or < signs.
Example in Biology:
- H₁: “Plants grown in sunlight have a higher average height than those grown in shade.”
- H₁: “The new drug significantly reduces blood pressure compared to a placebo.”
4. Purpose of Null and Alternative Hypotheses
- Null Hypothesis (H₀):
- Maintains the status quo.
- Provides a baseline for testing.
- Helps confirm consistency across experiments.
- Alternative Hypothesis (H₁):
- Suggests a new effect, relationship, or discovery.
- Provides direction for future research.
- Leads to new or improved theories if supported by evidence.
5. Principles Behind Hypothesis Testing
- Collect data from a sample representing the population.
- Apply statistical tests (e.g., t-test, chi-square, ANOVA).
- Compare results with a significance level (α), often set at 0.05 (5%).
- Use the p-value to decide whether to reject H₀.
- If p-value < α: Reject H₀ → Evidence supports H₁.
- If p-value > α: Fail to reject H₀ → Not enough evidence against H₀.
6. When to Reject Null Hypothesis?
The null hypothesis is rejected when the p-value is smaller than the chosen level of significance (e.g., 0.05).
- Reject H₀: Indicates that results are statistically significant and not due to chance.
- Fail to Reject H₀: Means there is not enough evidence, but it does not “prove” H₀ is true.
⚠️ Important: Failing to reject H₀ does not mean the null hypothesis is correct. It simply means there isn’t enough evidence to prove otherwise.
7. Examples of Null and Alternative Hypotheses
Example 1: Plant Growth
- H₀: “Fertilizer A does not affect plant height.”
- H₁: “Fertilizer A increases plant height.”
Example 2: Medicine Testing
- H₀: “Medicine X has no effect on reducing cholesterol levels.”
- H₁: “Medicine X reduces cholesterol levels significantly.”
Example 3: Gender Test Scores
- H₀: “The mean test scores of male and female students are equal.”
- H₁: “The mean test scores of male and female students are not equal.”
Mathematical form:
- H₀: μ₁ = μ₂
- H₁: μ₁ ≠ μ₂
8. Differences Between Null Hypothesis and Alternative Hypothesis
| Aspect | Null Hypothesis (H₀) | Alternative Hypothesis (H₁ / Ha) |
| Definition | Assumes no effect or no difference. | Assumes a real effect or difference exists. |
| Symbol | H₀ | H₁ or Ha |
| Mathematical Expression | Uses “=” (equals). | Uses “≠, <, or >”. |
| Nature | Conservative – tries to disprove. | Progressive – tries to prove. |
| Observation | Results are due to chance. | Results are due to real causes. |
| Result | No change in opinion or action. | Leads to new conclusions or actions. |
| Data Significance | If accepted → results are not significant. | If accepted → results are statistically significant. |
| Acceptance Rule | Accepted if p-value > α. | Accepted if p-value < α. |
| Importance | Confirms existing theories and ensures consistency. | Helps establish new theories and discoveries. |
9. Importance of hypotheses in Biological Research
In biology, hypothesis testing is crucial because it:
- Helps test the effectiveness of new medicines.
- Verifies differences in species behavior or physiology.
- Confirms whether environmental changes affect ecosystems.
- Ensures results are statistically valid and not due to random variation.
Example in Biology:
Testing whether a new pesticide affects insect mortality rates compared to untreated groups.
10. Common Mistakes in Understanding Hypotheses
- Believing that failing to reject H₀ proves it true.
- Using vague or unclear hypotheses (hypotheses should be specific and testable).
- Ignoring sample size – too small a sample may give misleading results.
- Forgetting that statistical significance ≠ practical significance (a small difference may be statistically significant but not biologically meaningful).
11. Tips for Writing Good Hypotheses
- State clearly whether you expect a difference or not.
- Use simple, testable, and measurable terms.
- Always write both null and alternative hypotheses.
- Ensure hypotheses match the research objectives.
- Avoid bias – hypotheses should not be framed to “force” a result.
Conclusion
The null hypothesis (H₀) and alternative hypothesis (H₁ or Ha) are fundamental concepts in scientific research.
- Null hypothesis assumes no difference or relationship.
- Alternative hypothesis assumes that a difference or relationship exists.
Together, they form the basis of hypothesis testing, helping researchers in biology and other sciences validate results, test theories, and make reliable conclusions.
By correctly setting up and testing hypotheses, researchers ensure their studies are scientifically sound, unbiased, and statistically significant.
References
- R. Kothari (1990) Research Methodology. Vishwa Prakasan. India.
- https://www.statisticssolutions.com/null-hypothesis-and-alternative-hypothesis/
- https://en.wikipedia.org/wiki/Null_hypothesis
- https://keydifferences.com/difference-between-null-and-alternative-hypothesis.html