When do I use a chi-square test in CFA Level I, and how do I set up the hypothesis for variance testing?
I keep seeing chi-square tests pop up in two different contexts — testing a single population variance and goodness-of-fit. For CFA Level I, which application is more important? Can someone walk me through a variance test with an actual example and show me how to find the critical values?
For CFA Level I, the chi-square test is primarily tested in the context of hypothesis tests about a single population variance (or standard deviation). The goodness-of-fit application appears less frequently at Level I but is worth knowing conceptually.
Chi-Square Test for Variance:
The test statistic is:
χ² = (n - 1) × s² / σ₀²
Where:
- n = sample size
- s² = sample variance
- σ₀² = hypothesized population variance
- Degrees of freedom = n - 1
The chi-square distribution is right-skewed and non-negative, which means the critical value approach differs from the symmetric z-test or t-test.
Worked Example — Hargrove Fixed Income Fund
The compliance team at Hargrove Asset Management claims the monthly return volatility (standard deviation) of their bond fund is no more than 1.8%. A risk analyst collects 25 months of returns and calculates a sample standard deviation of 2.3%. Test at the 5% significance level.
Step 1 — Hypotheses:
- H₀: σ² ≤ (0.018)² = 0.000324
- H₁: σ² > 0.000324 (one-tailed, upper tail)
Step 2 — Test Statistic:
χ² = (25 - 1) × (0.023)² / (0.018)² = 24 × 0.000529 / 0.000324 = 39.19
Step 3 — Critical Value:
For α = 0.05, df = 24, the upper-tail critical value is χ²₀.₀₅ = 36.415
Step 4 — Decision:
39.19 > 36.415 → Reject H₀. There is sufficient evidence that the fund's volatility exceeds the claimed 1.8%.
Important Properties:
- The chi-square distribution is bounded below by zero
- It is not symmetric — for two-tailed tests, you need both upper and lower critical values separately
- As df increases, the distribution approaches normality
- The test assumes the underlying population is normally distributed (sensitive to this assumption)
Exam Tip: When you see a question about testing whether a portfolio's risk exceeds or falls below a stated benchmark, think chi-square. Remember to square the standard deviations to get variances before plugging into the formula.
For additional hypothesis testing practice, check out our CFA Level I study materials.
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