how you calculate a discrete probability distribution depends on your test what you re trying to measure and how you measure it for instance if you re flipping a coin twice the possible combinations are 
because you re flipping the coin twice and there are two possible outcomes there are four possibilities each of the results represents one quarter of the possibilities the ht and th combinations are each one quarter and essentially the same thing representing one half of the results therefore the probability is that one quarter of the time you ll get a tt or hh and one half of the time you ll get ht or th this works similarly for rolling two dice because the results of a dice roll are discrete there are 36 possibilities because each die has six faces but there cannot be a result of one since the lowest number on each die is one so the lowest result you can get is two and the highest is 12 many of the combinations will repeat just as in the coin example so the more possibilities that repeat the more instances will be graphed as seen in the table below if you add the figures for dice roll results together you have one instance where the result is two and one where it is 12 creating odds of one in 36 for the numbers two and 12 the probability p that x the outcome will equal x the chosen number would be the probability that the roll equals two is one in 36 the probability of it equalling three is two in 36 and so on in the binomial tree model below the analyst has chosen intervals of three months with a starting price of 10 they have used past data from the investment to calculate the probability that the price will increase or decrease in the same way that the dice rolls were calculated in this image the analyst worked out that the probability of the price rising to 12 is 1 03 the probability that the price will drop to 8 is 3 43 from each increase or decrease in price you can see the analysts has worked out the discrete probabilities for nine months at the end of nine months you see that the probability of the stock price rising to 17 28 is zero while the probability of it dropping to 7 68 is 4 32 the probability of it reaching 5 12 is 6 98 so the stock is more likely to drop in price over the next nine months than it is to increase image by sabrina jiang investopedia 2020discrete distribution vs continous distributionif a discrete distribution is one that graphs discrete variables then a continuous distribution is one that graphs continuous variables the difference can be seen on graphs where discrete probability distributions are generally represented by bars because the data is discrete continuous probability distributions generally appear as a curve or a line on a graph because the data under the line is continuous and not finite 