The alternative hypothesis, denoted by Ha (sometimes H1), is the other side of the coin in hypothesis testing. It represents the opposite of the null hypothesis (H₀) and proposes the scenario you’re actually interested in investigating. Here’s a breakdown of key points about the alternative hypothesis:
- The “Interesting” Hypothesis: Unlike the null hypothesis (H₀) which assumes no effect, the alternative hypothesis (Ha) is the one you truly want to prove or disprove. It’s the idea you have about how the variables might be related.
- Example: Continuing with the plant fertilizer example, the null hypothesis (H₀) might be “There is no difference in plant growth…” The alternative hypothesis (Ha) would then be its opposite: “There is a difference in plant growth between plants using the new fertilizer and those using a standard fertilizer,” or more specifically, “Plants using the new fertilizer will grow taller than plants using the standard fertilizer.”
- Focus on Direction or Relationship: The alternative hypothesis often specifies the direction of the effect (taller plants) or the nature of the relationship (a difference in growth). It’s not simply “there is an effect,” but rather “there is a specific effect” you’re looking for.
- Testing and Evidence: The hypothesis testing process revolves around evaluating evidence against the null hypothesis. But ultimately, you’re hoping to gather data strong enough to reject H₀ and provide support for the alternative hypothesis (Ha).
Here are some additional points to consider:
- Wording of Ha: The alternative hypothesis should be clear and specific about the direction or nature of the effect you expect to see.
- Not Always Proven: Even if you reject the null hypothesis, it doesn’t necessarily guarantee Ha is absolutely true. It just means the evidence suggests an effect exists. Further investigation might be needed to pinpoint the exact nature of that effect.
In essence, the alternative hypothesis (Ha) is the driving force behind your research. It’s the idea you set out to test, and by examining evidence against the null hypothesis (H₀), you can see if your hunch about the variables holds water.