Monthly Archives: March 2016

The limitations of p values

A recent Nature commentary highlighted a statement by the American Statistical Association on principles to guide the use of p values for interpretation of research findings. The statement was issued out of concern for the lack of understanding of p values and what they imply. Specifically, the 6 principles of the statement are: P values can indicate how incompatible the

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Python: Data structures

Python has a number of in-built data structures. Two of the most common structures are lists and dictionaries. In Python, a list is a sequence of values and is constructed with square brackets, e.g. [1, 5, ‘abc’, 34.8]. The values in a list can be numbers or strings. A dictionary is a sequence of keys associated with values and is

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Python lessons: introduction

Python is a versatile, open source programming language for scientific computing and general-purpose programming. Some advantages that Python offers over other programming languages are (1) the syntax is relatively simple and readable, (2) Python handles numeric, text and other forms of data very well, (3) many libraries for scientific computing are available, and (4) there is a large and active

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Statistics note: What are confidence intervals?

In the previous post we learned that the standard error of a sample statistic indicates how precisely a population parameter is estimated, and the standard error depends on sample size. For a given standard deviation, a small standard error is obtained when sample size is large. The standard error indicates how variable the sample statistic is if the experiment is

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