In a sample of 1000 students in a university, 125 of them or 12.5% are biology majors. The 12.5% is an example of statistical inference.

Statistical inference is the process of using data from a sample to make generalizations or predictions about a larger population. In the example provided, where 125 out of 1000 students are biology majors, the percentage calculated (12.5%) reflects a characteristic of the sample. This percentage can lead to inferences about the population of all university students, suggesting that approximately 12.5% of the entire student body may also be biology majors, assuming the sample is representative of the population.

To clarify further:

1. Sample and Population: The 1000 students represent a sample of the larger population of all students at the university. The 125 biology majors are part of that sample.

2. Percentage Calculation: The percentage of biology majors in the sample is calculated as follows: $\text{Percentage of Biology Majors} = \left(\frac{\text{Number of Biology Majors}}{\text{Total Sample Size}}\right) \times 100 = \left(\frac{125}{1000}\right) \times 100 = 12.5\%$

3. General Inference: From this data, one might infer that if we took a larger sample of students, we could expect that around 12.5% of those students would also be biology majors.

4. Limitations: Statistical inference relies on the assumption that the sample is representative of the population. If there are biases or other factors affecting the sample, the inference may not hold true.

In conclusion, the calculated percentage of 12.5% is a key aspect of statistical inference, allowing us to make predictions or assumptions about the broader university student population based on the sample data.