I don't know much of sequential tests and their application outside of interim analysis (Jennison and Turnbull, 2000) and computerized adaptive testing (van der Linden and Glas, 2010). One exception is in some fMRI studies that are associated to large costs and difficulty to enroll subjects. Basically, in this case sequential testing primarily aims at stopping the experiment earlier. So, I am not surprised that these very tailored approaches are not taught in usual statistical classes.
Sequential tests are not without their pitfalls, though (type I and II error have to be specified in advance, choice of the stopping rule and multiple look at results should be justified, p-values are not uniformly distributed under the null as in a fixed sample design, etc.). In most design, we work with a pre-specified experimental setting or a preliminary power study was carried out, to optimize some kind of cost-effectiveness criterion, in which case standard testing procedures apply.
I found, however, the following paper from Maik Dierkes about fixed vs. open sample design very interesting: A claim for sequential designs of experiments.
One thing I have done with students that went over well was to take several packages (the small ones) of M and M's candy and have the students count how many of each color there is in a pack (depending on the number of students they may each get their own or work in groups of 2 or 3). The students can usually figure an appropriate way to dispose of the candies afterwards. If you want more data, or comparisons, or just the "Population Proportions" I have recorded some values here (if you do this consider submitting your data to add).
Then you can use the data that they have just collected to show some basic concepts like variation (they did not all get the same counts/proportions). You can show some basic graphics like a histogram of the proportion of Blue candies, or boxplots comparing the proportions of a color from different types.
I then usually show them the true proportion for one of the colors and show how their proportions, while not exactly the truth, tend to cluster around the true value. I then show how close they tend to be to the truth (a general rule of thumb says that for a sample size of 50 the 95% margin of error will be about 14-15%). Then I show them the proportion of a different color from one of their samples and ask what values of the "truth" would be believable (using the 14-15% rule of thumb again) without telling them what the truth is. This gives a general idea of the concept of a confidence interval.
Another option is living graphs, have each of the students know some numeric fact about themselves (height in inches/cm works well). Clear a space on the floor and put some masking tape down with values written on it (like the axis of a plot). Have the students line up next to their value. You can then climb up on a desk/ladder and take a picture of the living histogram (I have seen this done outside with a tall ladder for a really good effect). Then you can have them count off from each end and put down a stripe of tape where they meet in the middle (the median), then do the same for each half and put down tape for the quartiles, wrap the tape around the middle half, then have them lower that to the floor, add the wiskers and have them step away to see the boxplot remaining on the floor. If there are enough students you could have them do this separately for boys and girls and compare the boxplots.
An activity to show the need to take good samples and avoid biased sampling can be done by getting some regular drinking straws and cutting them to lengths of 1 inch, 2 inches, and 4 inches. Put 4 of each length in a paper bag. Give a paper bag to each group of students and have them take a sample of size 4 from each bag by reaching into the bag without looking and taking out 4 at random. Have each group put their straws back and take a few more samples. Record the means of their samples and create a histogram, show the real mean on the graph to show how their means tend to be larger on average than the truth due to the biased sampling.
You could also discuss some of the principles of study design by having the students make paper helicopters (you can google for templates) and vary some options (wing length, body width, paper clip or no paper clip, etc.) to see if they can find the design that takes the longest to fall a set distance. You can discuss replication, randomization of testing order (what if the wind changes during the testing period?) and other concepts.
Best Answer
Try to personalize statistics. To show why understanding its concepts (even though they will forget the math, acknowledge it) is useful to them. For instance, how to interpret breast cancer test results. To quote from http://yudkowsky.net/rational/bayes:
Since your students will be medical doctors, make it clear: if they don't understand statistics, they will give the wrong interpretation of the results to their patients. This is not an academical matter.
Also acknowledge that unless they go in research, they will forget the details you will teach them. Don't even hope it's not the case. Aim for them to understand the fundamental concepts (type I and II errors, correlations and causations and so on) so when faced with a situation, they will remember "hey, perhaps I shouldn't rush drawing a conclusion, but talk to someone who understand stats better." Preventing cognitive errors and teaching them to be inquisitive of the results provided by others (especially in an industry where large sums of money are at stake) will be signs you succeeded.