Framing null and alternative hypotheses are essential steps in statistical hypothesis testing. They establish the foundation for your investigation by setting up competing possibilities about a relationship between variables. Here’s a breakdown of what each hypothesis represents and how to frame them effectively:
Null Hypothesis (H₀):
- Represents: This hypothesis assumes “no effect” or “no difference” between the variables you’re studying. It acts as the baseline assumption, often representing the current understanding or established theory.
- Framing: The null hypothesis is typically denoted by H₀ (H-zero) and is formulated using an equal sign (=). For example, “There is no difference in plant growth between using fertilizer A and fertilizer B.”
Alternative Hypothesis (H₁):
- Represents: This hypothesis proposes the opposite of the null hypothesis. It reflects the prediction you’re actually interested in testing. There may be an effect, a difference, or a relationship between the variables.
- Framing: The alternative hypothesis is denoted by H₁ (H-one) and uses terms like “greater than” (>), “less than” (<), or “not equal to” (≠) depending on your prediction. Examples:
- “Plant growth is greater with fertilizer A compared to fertilizer B.” (one-tailed alternative)
- “Plant growth is different with fertilizer A compared to fertilizer B.” (two-tailed alternative)
Key Points to Remember:
- They are mutually exclusive: You can only accept one hypothesis at the end of your analysis. You either reject the null hypothesis (providing evidence for the alternative), or fail to reject it (meaning the null hypothesis remains plausible).
- Burden of proof lies on rejecting the null hypothesis: The null hypothesis is generally assumed to be true until there’s strong evidence to disprove it.
- Wording is crucial: Ensure your hypotheses are clear, concise, and directly related to the variables you’re investigating.
By carefully framing your null and alternative hypotheses, you set a clear direction for your statistical test and lay the groundwork for interpreting your results.