You can determine whether the normal distribution table is not enough for your data by checking for any outliers or extreme values. If your data has a large number of outliers, it may not follow a normal distribution and using the normal distribution table may not be appropriate.
There are several alternatives to using the normal distribution table, such as using non-parametric tests, transforming the data to make it more normally distributed, or using other statistical distributions that better fit the data.
In some cases, it may still be appropriate to use the normal distribution table even if your data is not perfectly normally distributed. However, if your data is significantly skewed or has a large number of outliers, it may be more appropriate to use alternative methods.
The central limit theorem states that the means of a large number of samples from any population will be normally distributed. This means that in most cases, using the normal distribution table is appropriate as long as your sample size is large enough.
Yes, there are some limitations to using the normal distribution table. It assumes that the data is normally distributed and may not be appropriate for highly skewed or non-normal data. Additionally, the normal distribution table is only applicable for continuous data, not categorical data.