In the short series on statistical inference, I ran simulations and plotted graphs to show distributions of raw data and statistics for repeated experiments. All simulations and plots were done with the programming language Python and its plotting library Matplotlib. What exactly are Python and Matplotlib, and how can these tools be used to visualise data? Python is an open
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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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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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In the previous post we learned that a sample statistic (e.g., a sample mean) is used to estimate a population parameter (e.g., the population mean), and the standard error of the sample statistic indicates the amount of precision around the estimate of the population parameter. A small standard error indicates that a sample statistic estimates a population parameter with high
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In the previous post we learned that inferential statistics uses information from individuals observed in a sample to provide information about other individuals in the population that were not observed. A description of the population provides information about all individuals. But how is a sample or population described, and how are sample data used to make inferences about a population?
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Scientists ask questions about the world, design experiments, and use experimental findings to work out answers. In an experiment, descriptive statistics are used to summarise and describe observations from a sample (e.g., honey bees in Paris, voters from 5 electorates). However scientists are more interested in the population from which the sample was drawn (e.g., all honey bees in Europe,
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Scientifically Sound 