**Introduction**

Skewness is a statistical measure that describes the asymmetry or lack of symmetry in a distribution. It helps us understand the shape of the distribution and the concentration of values around the mean. A positively skewed distribution has a long tail on the right side, while a negatively skewed distribution has a long tail on the left side. In this response, we will discuss the factors that do not indicate the skewness of a distribution.

**Factors that do not indicate the skewness of a distribution**

1. **Sample Size**: The size of the sample does not indicate the skewness of a distribution. Skewness is a characteristic of the data distribution itself and is not affected by the number of observations. Whether the data is positively skewed, negatively skewed, or symmetrical remains the same, regardless of the sample size.

2. **Mean**: The mean of a distribution does not directly indicate its skewness. The mean is a measure of central tendency and is influenced by extreme values. In a positively skewed distribution, the mean will be greater than the median, while in a negatively skewed distribution, the mean will be less than the median. However, there can be cases where the mean and median are close or equal in value, even in skewed distributions.

3. **Standard Deviation**: The standard deviation does not provide information about the skewness of a distribution. It measures the dispersion or spread of data around the mean. Skewness, on the other hand, focuses on the shape and asymmetry of the distribution. It is possible to have distributions with different levels of skewness but similar standard deviations.

4. **Range**: The range, which is the difference between the maximum and minimum values in a distribution, does not indicate the skewness. It only reflects the spread of data from the lowest to the highest value. Skewness, on the other hand, considers the concentration of values around the mean and the presence of tails on either side.

5. **Kurtosis**: Kurtosis is a measure of the peakedness or flatness of a distribution. It does not directly indicate the skewness. While some distributions may exhibit both skewness and kurtosis, they are independent characteristics of the data. A distribution can be skewed without being excessively peaked or flat, and vice versa.

**Conclusion**

In conclusion, the factors mentioned above do not indicate the skewness of a distribution. Skewness is a property of the data distribution itself and can be determined by analyzing the shape and tail behavior of the distribution. By understanding these factors and their relationship to skewness, we can better interpret and analyze data distributions.