Hypothesis Testing: Overview Example
Hypothesis Testing: Overview Example
The Seattle Welfare Office can handle 210 cases a day based on current budget and staffing levels. The office contends it gets more than that, on average, and that their budget should be increased accordingly.
Step 1) Clearly define the question
Is the Seattle Welfare office caseload greater than 210 clients per day, on average?
Step 2) Develop testable hypotheses
Create a null hypothesis exactly opposite of what you want to prove. Why? Because it is impossible to prove anything true (too many possible explanations/threats) so we prove something false and accept an alternative explanation. That alternative is our alternative hypothesis. (W&C calls it a research hypothesis).
Therefore, for this case: (hypotheses should be exhaustive and mutually exclusive, the null generally suggests no difference, and the alternate must be difference making.)
Ho: μ 210
Step 3) Collect data
Random and representative sample of caseloads over time such that x-bar approximates μ (sample mean approximates true population mean)
x-bar = 226
n = 61 days
s = 50
d.f. = n-1 = 60
Step 4) Determine boundaries of rejection of Ho (I call this tcritical, or tcrit)
Look to Ha for tails of test. < left-tailed > right tailed = two-tailed
t-table read t alpha, d.f. for t.05, 60 = +1.671 (look familiar? 1 std. deviation from mean)
graph rejection region:
Step 5) Calculate test statistic (I call this tcalculated, or tcalc)
In this case, it’s a single mean test with an unknown population mean, so:
t = [pic] or t = [pic] = 2.479
Step 6) Draw conclusions. Be able to do this in English, not just mathspeak. Your boss doesn’t understand mathspeak.
Sample tcalc is within the rejection region, which means we reject the null hypothesis, and must then accept the alternative. Meaning, we reject that the office gets less than or equal to 210 cases, and that at a 95% confidence level we find that the office gets more than 210 cases/day.
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