The count outcome samplespace for event the sample space

o (i) Discrete probability: The ratio of the number of times an event occurs to all conceivable outcomes of the experiment, according to Kadi et al. (2023), is the discrete probability of that occurrence. It is known as a contingency. It is indicated by the letters p or p(A) which stand for p times the event occurs/total number of
outcomes.

(ii) Continuous probability: The ratio of the frequency of an event to the entire number of experiment results, as in the case of a normal distribution, is the continuous probability of that occurrence. The occurrence is referred to as a contingency (Kadi et al., 2023). The symbol for it is p, or p(A) = total number of outcome that is divided by frequency of the occurrence.

With discrete probability, the result will only happen once, with a probability of 1/n that the event will happen (Yukalov & Yukalova, 2023). If the likelihood is continuous, the event will take on a variety of values, each with a probability ranging from zero to one.

Reference
Yukalov, V. I., & Yukalova, E. P. (2023). Discrete versus Continuous Algorithms in Dynamics of Affective Decision Making. Algorithms, 16(9), 416.

Different Forms of Hazard: Continuous and Discrete Version with Their Inferences Tests. Mathematics, 11(13), 2929.

Reply 2
Your discussion of discrete and continuous probability is correct. The main difference between the two is the measure of the probability. For discrete probability, the measure is in terms of the count outcomes of the event in the sample space. For continuous probability, the measure is in terms of the probability density function (pdf) of the events in the sample space (Curado & Nobre, 2023). This is how discrete probability and continuous probability are different and this is why continuous probability is more sophisticated.