

When entering data, simply leave a blank spot for any value that is missing. Excluded values are treated exactly the same as missing values.
Prism never ever treats an empty cell as if you had entered zero  it always knows that is a missing value. It will analyze the data if it can, and leave analysis results blank when it cannot.
The details of how Prism handles missing values differs for various statistical tests.
These tests work fine with unequal sample size. Missing values are not a problem. 
Prism only analyzes rows where there are data for all conditions. If one value is missing, that subject (row) is ignored. 
If some values are missing, twoway ANOVA calculations are challenging. If any row/column combinations have no values at all, Prism cannot compute ANOVA. If some row/column combinations have fewer replicates than others (some replicates are missing), Prism uses the method detailed in SA Glantz and BK Slinker. This method converts the ANOVA problem to a multiple regression problem and then displays the results as ANOVA. Prism performs multiple regression three times — each time presenting columns, rows, and interaction to the multiple regression procedure in a different order. Although it calculates each sumofsquares three times, Prism only displays the sumofsquares for the factor entered last into the multiple regression equation. These are called Type III sumofsquares. 
If your data are balanced (same sample size for each condition), entering data as mean, SD (or SEM) and N is not a problem  you'll get the same results as if you had entered raw data. However, this not the case if your data are unbalanced, it is impossible to calculate precise results from unbalanced data entered as mean, SD (or SEM), and N. Instead, Prism uses a simpler method called analysis of “unweighted means”. This method is detailed in LD Fisher and G vanBelle (details below). If sample size is the same in all groups, and in some other special cases, this simpler method gives exactly the same results as obtained by analysis of the raw data. In other cases, however, the results will only be approximately correct. If your data are almost balanced (just one or a few missing values), the approximation is a good one. When data are unbalanced, you should enter individual replicates whenever possible. 
Prism cannot perform repeatedmeasures twoway ANOVA if any values are missing. Prism (beginning with Prism 5) works fine if there are different numbers of numbers of subjects in each group, so long as you have complete data (at each time point or dose) for each subject. Say you are comparing two groups (control and treated) measured at four time points. It would be fine if there were more treated subjects than control subjects, so long as each subject has data at all four time points. But Prism can not analyze repeated measures twoway ANOVA if one of the subjects only had data for three time points, with the fourth time point missing. . 
Fitting lines and curves works fine with missing values. You can choose whether Prism fits the individual replicates or fits the means. If you choose to fit the means, each mean gets the same weight regardless of how many values were used to compute it. If you fit the individual replicates, then X values with more Y replicates get more weight than X values with fewer replicates. 
Comparison of survival curves does not require equal sample size. If data are completely missing for any subject, simply don't enter data for that subject. But before deciding to leave data out, read about censoring which happens when you know the subject survived up until a certain time, but don't know what happened after that (or you know, but can't use the data because the experimental protocol wasn't followed). Prism handles censored data fine. Don't omit those subjects, enter the duration that they survived on the experimental protocol and mark that duration as censored. 