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How to Write a Null Hypothesis for WGU C207 Task 1

Of all the individual rubric criteria in WGU C207 Task 1, the null hypothesis statement is the one I see students overthink the most. It’s usually one sentence, but it needs to say a very specific thing in a very specific way — and if it’s phrased wrong, it can throw off the interpretation you write later in section D, even if your actual regression output is fine.

Here’s how to get it right the first time.

What the null hypothesis is actually claiming

In the context of a simple linear regression, the null hypothesis isn’t a vague statement about “no relationship” in general — it’s a precise claim about one specific number: the slope coefficient of your regression line, usually written as β₁ (beta-one).

The null hypothesis states that β₁ = 0. In plain English: your independent variable has no effect on your dependent variable — knowing someone’s value on X tells you nothing about their expected value on Y.

The alternative hypothesis is the direct opposite: β₁ ≠ 0 — there is a real, non-zero relationship between your two variables.

This matters because your entire regression analysis exists to test which of these two claims your data supports.

Why “no relationship” isn’t specific enough

A common mistake is writing something like:

H0: There is no relationship between nurse wellness program participation and nurse attrition.

This isn’t wrong exactly, but it’s imprecise for a task that’s specifically testing your understanding of regression. WGU’s rubric is looking for language that ties the hypothesis directly to the regression coefficient, not just a general claim about association. A stronger version:

H0: The wellness program participation rate has no statistically significant effect on nurse attrition rate (β1 = 0). Ha: The wellness program participation rate has a statistically significant effect on nurse attrition rate (β1 ≠ 0).

Notice the difference: this version names your actual variables, ties the claim explicitly to the regression coefficient, and uses the word “statistically significant” — which signals you understand this is a hypothesis test, not just a description of a pattern.

Directional vs. non-directional hypotheses

You’ll also need to decide whether your alternative hypothesis is directional (specifies whether the effect is positive or negative) or non-directional (just claims an effect exists, either direction). For Task 1, unless your specific business question explicitly asks you to predict a direction beforehand, a non-directional alternative hypothesis (β1 ≠ 0) is usually the safer, more defensible choice — it’s testing whether a relationship exists at all, which is what a two-tailed p-value from Excel’s regression output is actually built to test.

If you do write a directional hypothesis (e.g., “participation rate has a negative effect on attrition rate”), make sure your interpretation in section D matches that direction — a common point-loss is writing a directional hypothesis and then not explicitly confirming the direction of the relationship matched your prediction.

Connecting the hypothesis to your eventual decision

The reason this sentence matters beyond the rubric checkbox is that it sets up the reject/fail-to-reject decision you’ll write later. Once your regression output gives you a p-value, you compare it against your chosen significance level (almost always α = 0.05 for this task):

If p < 0.05 → you reject the null hypothesis → there is a statistically significant relationship
If p ≥ 0.05 → you fail to reject the null hypothesis → you don’t have enough evidence of a relationship

This is why the null hypothesis needs to be worded precisely: your reject/fail-to-reject sentence later needs to refer back to it directly, and vague wording up front makes that sentence harder to write cleanly.

A worked example: Crestview Regional Hospital

Using the same fictitious Crestview Regional Hospital scenario referenced in the main Task 1 guide — a hospital examining whether its nurse wellness program participation rate predicts its nurse attrition rate — a complete hypothesis pair looks like this:

Business question: Does the nurse wellness program participation rate predict the nurse attrition rate at Crestview Regional Hospital?

H0: Nurse wellness program participation rate has no statistically significant effect on nurse attrition rate (β1 = 0).

Ha: Nurse wellness program participation rate has a statistically significant effect on nurse attrition rate (β1 ≠ 0).

With a fictitious p-value of 0.0002 (well below α = 0.05) from the regression output, the conclusion would read:

Because p = 0.0002 is less than α = 0.05, we reject the null hypothesis. There is sufficient evidence to conclude that nurse wellness program participation rate has a statistically significant effect on nurse attrition rate at Crestview Regional Hospital.

(As with any worked example tied to this course, these specific numbers are illustrative — your own dataset will produce a different p-value, and your conclusion sentence needs to reflect your actual output, not this one.)

Related to the WGU C207 Task 1 Guide

WGU C207 Task 1: Complete Guide + Worked Example

How to Run a Linear Regression in Excel for WGU C207 Task 1

How to Interpret Your WGU C207 Task 1 Regression Output

Common mistakes to avoid

Writing the hypothesis in terms of correlation instead of regression. “There is no correlation between X and Y” describes a different (though related) statistic than the regression slope coefficient. Stick to language tied to β1 for this task.
Forgetting to state both H0 and Ha. Some students write only the null and imply the alternative — the rubric expects both stated explicitly.
Mismatching the hypothesis direction and the later interpretation. If you write a directional alternative hypothesis, your D-section interpretation needs to explicitly address whether the direction matched.
Restating the business question instead of the statistical hypothesis. These are related but not the same sentence — the business question is about the real-world scenario; the hypothesis is a formal statistical claim about β1.

Third-Party Resources

Understanding the Null Hypothesis for Linear Regression — Statology — a clear walkthrough of the H0/Ha framework specifically for simple linear regression, including worked notation examples.
Laerd Statistics — broader statistical methodology reference, useful if you want more background on hypothesis testing conventions before writing your own.

Frequently Asked Questions

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Do I need to state a specific alpha level, or is 0.05 assumed? State it explicitly. Even though 0.05 is the near-universal default and likely what your course materials use, writing “at α = 0.05” in both your hypothesis section and your conclusion sentence removes any ambiguity about what threshold you’re testing against.

Can my null and alternative hypotheses be more than one sentence each? They can, but shorter is usually better here — a precise single sentence per hypothesis is easier for an evaluator to check against the rubric than a longer, hedged explanation.

What if my p-value comes out exactly at 0.05? This is rare with real data but worth knowing: the standard convention is that p ≥ α means you fail to reject the null, so a p-value of exactly 0.05 would technically fail to reject under the strict rule, though this exact-boundary case essentially never comes up in practice.

For the full rubric breakdown, Excel walkthrough, and business-question framing for this task, see the complete WGU C207 Task 1 guide.

Stuck on how your specific hypothesis should read? Message me on WhatsApp and I’ll help you work through it.

Dan Palmer, MBA, writes WGU MBA course guides for Gradevia, focusing on the quantitative and analytics-heavy courses (C207, C211, C213, C214). Connect on LinkedIn.