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It looks like you've uploaded an image containing a problem related to a statistical table showing the success rates of treatments for male and female patients. Let me extract the key details from the image and help you solve the problem:

### The Table (Treatment Success Data):
The table shows the success rates for male and female patients undergoing two treatment types, X and Y.

- **For Males:**
  - Treatment X: 5 successes out of 15 trials (Success Rate: 33%)
  - Treatment Y: 10 successes out of 10 trials (Success Rate: 100%)

- **For Females:**
  - Treatment X: 7 successes out of 10 trials (Success Rate: 70%)
  - Treatment Y: 0 successes out of 5 trials (Success Rate: 0%)

There are missing values in the data, and the question asks you to determine which statement must be true based on the table and information provided.

### The Statements:
1. **Simpson's Paradox** occurs when a third variable (gender, in this case) influences the relationship between the treatment type (X or Y) and the outcome (success or failure).
2. Treatment type is a confounder between the variables (gender and outcome).

### Correct Answer:
From the problem and the setup of the table, **Simpson’s Paradox** could be present because when you combine data across both genders, the success rate of a treatment (such as Treatment Y) might look better or worse depending on the distribution of males and females in each treatment group. Gender affects the interpretation of treatment success.

Therefore, the correct answer is **Option 1 (Only 1)**: Simpson's Paradox is observed.

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Would you like more details or have any questions? 

Here are 5 related questions to further explore the concept:
1. What is Simpson's Paradox and how does it apply in this case?
2. How can we detect a confounder in data analysis?
3. What are the implications of Simpson’s Paradox in real-world decision-making?
4. Can Simpson’s Paradox occur with more than two variables?
5. How does the sample size of each group affect the interpretation of statistical data?

**Tip:** Always be careful when interpreting combined data across groups, as hidden variables can sometimes reverse the observed trend.

The table below shows male and female patients undergoing two treatment types, X or Y. The success rate of the treatment is designated as either successful or unsuccessful. The success rates of the respective treatments across genders are also calculated. Unfortunately, some of the data is missing. We know that all missing values are non-zero. Which of the following statements must be true?

Analyze the success rates of treatments for male and female patients in this statistics problem. Learn about Simpson's Paradox and confounding variables when interpreting incomplete data, and discover which statement must be true based on the table provided. Ideal for undergraduate statistics students.

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