Some words have particularly high probabilities of occurring in spam emails. For example, "Viagra" is frequently seen in spam emails, but seldom used in non-spam emails. The "Bayes spam filtering" is an email filtering method based on this idea. To implement this, the conditional probability of a spam email for each word has to be calculated first. A random sample of 1,000 emails are collected and reviewed if the email contains a certain word and if the email is spam. The results are summarized in the following probability distribution table. Spam No Containing the word 4% 24% Not containing the word 16% 56% Which email has a higher chance of being a spam email, containing the word or not containing the word? a. An email containing the word has a higher chance of being a spam email than one NOT containing the word. D. An email NOT containing the word has a higher chance of being a spam email than one containing the word. C. There is no difference. The information is not enough to compare them