Example: Tulips Section
A company selling tulip bulbs claims they have equal proportions of white, pink, and purple bulbs and that they fill customer orders by randomly selecting bulbs from the population of all of their bulbs.
You ordered 30 bulbs and received 16 white, 8 pink, and 6 purple.
Is there convincing evidence the bulbs you received were not randomly selected from a population with an equal proportion of each color?
Use Minitab to conduct a hypothesis test to address this research question.
We'll go through each of the steps in the hypotheses test:
\(H_0\colon p_{white}=p_{pink}=p_{purple}=\dfrac{1}{3}\)
\(H_a\colon\) at least one \(p_i\) is not \(\dfrac{1}{3}\)
We can use the null hypothesis to check the assumption that all expected counts are at least 5.
\(Expected\;count=n (p_i)\)
All \(p_i\) are \(\frac{1}{3}\). \(30(\frac{1}{3})=10\), thus this assumption is met and we can approximate the sampling distribution using the chi-square distribution.
Let's use Minitab to calculate this.
First, enter the summarized data into a Minitab Worksheet.
C1 | C2 | |
---|---|---|
Color | Count | |
1 | White | 16 |
2 | Pink | 8 |
3 | Purple | 6 |
- After entering the data, select Stat > Tables > Chi-Square Goodness of Fit Test (One Variable)
- Double-click Count to enter it into the Observed Counts box
- Double-click Color to enter it into the Category names (optional) box
- Click OK
This should result in the following output:
Chi-Square Goodness-of-Fit Test: Count
Observed and Expected Counts
Category | Observed | Test | Expected | Contribution |
---|---|---|---|---|
White | 16 | 0.333333 | 10 | 3.6 |
Pink | 8 | 0.333333 | 10 | 0.4 |
Purple | 6 | 0.333333 | 10 | 1.6 |
Chi-Square Test
N | DF | Chi-Sq | P-Value |
---|---|---|---|
30 | 2 | 5.6 | 0.061 |
The test statistic is a Chi-Square of 5.6.
The p-value from the output is 0.061.
\(p>0.05\) therefore we fail to reject the null hypothesis.
There is not enough evidence that the tulip bulbs were not randomly selected from a population with equal proportions of white, pink and purple.