Which algorithm suits this type of outlier detection? Suppose I have gathered the time a certain user takes to input a four digit PIN from his previous logins as follows : 
User A : (10,12,11,13,19.1,12.4,12,16)
Now, User A wants to login again to perform a transaction. This time he took 11.03 to input the four digit PIN. As of now, I found Extreme Studentized Deviate  that it can be used to detect outliers for univariate data, but am not sure of its performance.
Question:


*

*Which method or approach can I use to detect whether 11.03 is an outlier?

*What others have done?

*Can I use LOF? If so how? A little light will do. Thanks.
PS: Units of time in this case are not important, they are just random values for demonstrating the concept.
 A: Thinking beyond the statistics...
I imagine the goal here is to say: The user took too long to enter the PIN compared to their usual time, so it is likely to be someone else using the password. 
But.. Maybe I took longer because I was carrying a baby in my right arm, so had to enter the PIN with my left (non dominant) hand. Or maybe I was outside wearing gloves. Or maybe I got interrupted or was trying to carry on a conversation while entering the PIN. There are lots of reasons why I may enter a PIN slower than usual, so I think it would be a bad way to detect fraud. If you follow this logic, the outlier tests won't be helpful.
A: In my opinion, if the data is a time series data, forecasting based confidence intervals give a good idea of whether a point is an outlier or not.
For non time series univariate data, multiple methods can be tried out -  


*

*Z-score based method(this resembles your idea of using points farther from the expected value as outliers)

*Tukey's Method

*MAD(Median Absolute Deviation)


Out of the above methods for univariate data, I would recommend tukey's method because it is relatively robust.
For Multivariate Data, I would recommend methods like LOF, Elliptic Envelope(which uses the Mahalanobis distance internally - implemented in scikit-learn).
A: An outlier can be defined as "an observation point that is distant from other observations" . I prefer "an observation point that is distant from expectatations" . I took one of our "teaching moments series" and used your reference  http://www.graphpad.com/quickcalcs/grubbs2/ to obtain  a conclusion about "normalcy of the last observation". I took your 9 values and found that while the most recent values was "normal" two prior values were deemed exceptional  and  . As @forecaster pointed out the work of Tsay and others is often useful. 
