Predicting number of events with 99.9% probability based on tests of four devices I've never taken a stats course so I really don't know where to begin on this.
I am using microcontroller that according to the datasheet, has a Flash memory that can be reprogrammed a minimum of 10,000 times, with 100,000 times being a more typical number.
These numbers seem low to me (based on other devices using a similar technology which claim lifetimes of an order of magnitude higher), so I decided to take one of our products and do destructive testing on it.  I started testing last Friday night and it is already up to 600,000 events without an error (as determined by calculating a 16-bit CRC over the 4K bytes of data test being Flashed).  Based on this device far surpassing the "typical" value in the datasheet, I have started tests on three more devices.
So at some point I will have a failure value for all four devices.  How can I use that data to predict an expected lifetime x for the device with a 99.9% certainty?  (Not sure if I am saying that right, what I mean is that only 0.1% of devices will fail before x events).
 A: @whuber makes an important point about assumptions,
It is common to assume that failure occurs either because a part is defective or because it has worn out. This means that there are two processes causing failure, and defective units fail shortly after deployment whereas wearing out happens over time. It is difficult (or impossible) to justify parameterizing both of these processes if $n=4$.
Case 1
For a simple case, assume that defective units can be eliminated (e.g. by an initial screening or 'burn in'), and all units fail due to a constant wearing process. This assumption may be valid since the data sheet says that the minimum expected lifetime is $10,000$ events. One might model this process as an exponential distribution:
Example: assume that your four test units fail after $[5,10,20,40]x10^{5}$ reprogrammings. You can find the maximum likelihood estimate for the exponential distribution in R:
library(MASS)
fitdistr(c(5, 10, 20, 40)*1e+5, 'exponential')
lambda <- fitdistr(c(5, 10, 20, 40)*1e+5, 'exponential')$estimate

To find the time during which only 1/1000 units will fail, estimate the 0.1th quantile of this distribution.
qexp(0.001, lambda)

In this example, the result is an expected lifetime of 1875.
edit As whuber points out, $n=4$ is still to small for this problem, because it is very difficult to estimate small probabilities, and distribution tails are very sensitive to assumptions about the probability distributions used to model the data, and to the data itself. 
Since you have good prior knowledge from the product datasheet, you could incorporate this information into your analysis as a prior on lambda. The gamma distribution is a conjugate prior, $\lambda\sim \text{Gamma}(\alpha, \beta)$, but I can not find a parameterization appropriate to the prior information, with $\text{median}\simeq 100,000$, $P(Y<10,000)\simeq 0)$ and nonzero density above $10,000$. Once you determine the model to use, it would be worth asking a separate question.
Case 2
If you assume that failure is caused either by defect or by wear, you could consider the Weibull and Generalized Exponential distributions (Gupta and Kunda 1999, 2001, 2007). The field of survival analysis provides many options, but as whuber points out, $n=4$ is insufficient to justify a model other than, perhaps, the single parameter exponential.

Gupta, R. D. and Kundu, D. (1999). Generalized exponential distributions", Australian and New Zealand Journal of Statistics, vol. 41, 173 - 188.
Gupta, R. D. and Kundu, D. (2001), Generalized exponential distributions: different methods of estimation", Journal of Statistical Computation and Simulation. vol. 69, 315-338.
Gupta, R. D. and Kundu, D. 2007. Generalized Exponential Distribution: existing results and some recent developments. Journal of Statistical Planning and Inference
