Can you list the issues with using a symmetric probability distribution when modelling problems?

There are several issues with using a symmetric probability distribution when modeling problems : \n \n 1 . Sym metric distributions are often assumed to be normal distributions , which may not always be the case . For example , some data may have a ske wed distribution , which cannot be accurately model ed using a symmetric distribution . \n 2 . Sym metric distributions may not capture the full range of possible outcomes . For example , if the data is bounded , a symmetric distribution may not be able to capture the full range of possible outcomes . \n 3 . Sym metric distributions may not be appropriate for certain types of data . For example , if the data is discrete , a symmetric distribution may not be appropriate . \n 4 . Sym metric distributions may not be able to capture the asym metry in the data . For example , if the data has a long tail , a symmetric distribution may not be able to capture the full range of possible outcomes . \n 5 . Sym metric distributions may not be able to capture the asym metry in the data . For example , if the data has a long tail , a symmetric distribution may not be able to capture the full range of possible outcomes . \n 6 . Sym metric distributions may not be able to capture the asym metry in the data . For example , if the data has a long tail , a symmetric distribution may not be able to capture the full range of possible outcomes . \n 7 . Sym metric distributions may not be able to capture the asym metry in the data . For example , if the data has a long tail , a symmetric distribution may not be able to capture the full range of possible outcomes . \n 8 . Sym metric distributions may not be able to capture the asym metry in the data . For example , if the data has a long tail , a symmetric distribution may not be able to capture the full range of possible outcomes . \n 9 . Sym metric distributions may not be able to capture the asym metry in the data . For example , if the data has a long tail , a symmetric distribution may not be able to capture the full range of possible outcomes . \n 1 0 . Sym metric distributions may not be able to capture the asym metry in the data . For example , if the data has a long tail , a symmetric distribution may not be able to capture the full range of possible outcomes . </s>