 The Z-score is a statistical measurement that describes a value’s relationship to the mean of a group of values. It is commonly used in the field of statistics to determine how far away a particular data point is from the mean. The Z-score allows for easy comparison of different data points, as it standardizes the values. In a standard normal distribution, the mean is zero and the standard deviation is one.

The 25th percentile of the standard normal distribution refers to the value below which 25% of the data falls. To find the Z-score for the 25th percentile, you need to find the value that corresponds to the cumulative probability of 0.25. Using a Z-score table or a statistical calculator, you can determine that the Z-score for the 25th percentile is approximately -0.674. This means that 25% of the data falls below a value that is 0.674 standard deviations below the mean.

Here are some frequently asked questions about the Z-score for the 25th percentile of the standard normal distribution:

1. What does the Z-score for the 25th percentile represent?
The Z-score for the 25th percentile represents the number of standard deviations below the mean that a particular value falls.

2. How is the Z-score for the 25th percentile calculated?
The Z-score for the 25th percentile is calculated by finding the value that corresponds to a cumulative probability of 0.25 in the standard normal distribution.

3. What if the data is not normally distributed?
If the data is not normally distributed, the Z-score may not accurately represent the relationship between a value and the mean.

4. Can the Z-score for the 25th percentile be negative?
Yes, the Z-score for the 25th percentile can be negative if the value falls below the mean.

5. What is the significance of the 25th percentile?
The 25th percentile represents the value below which 25% of the data falls, indicating the lower end of the data distribution.

6. How does the Z-score for the 25th percentile help in statistical analysis?
The Z-score for the 25th percentile allows for easy comparison of data points and helps in identifying outliers or unusual values.