In this short course you look at a table of data, sort data into its kinds, and learn how to ask fair questions and choose a fair sample. The code boxes only need Run and a few changed numbers, so you do not need to know Python.
3 lessons, about 1 h 25 min3 simulations10 problems6 practice questionsPython in every lesson
Data is a set of facts that we collect, such as heights, colours, marks or counts, and statistics is what we do to make sense of it. We keep data in a table, where each row is one case, such as a student or a day, and each column is one variable. The number of facts in a table is rows times columns. Data comes from asking, measuring, counting or looking up records. A good table has one fact in each cell, the same unit in a column, and a clear mark for a missing value.
Data is categorical, in words or labels, or numerical, in numbers. Categorical data is nominal if the labels have no order, such as colour, and ordinal if they have an order, such as shirt size. Numerical data is discrete if we count it in whole steps, such as siblings, and continuous if we measure it and it can take any value in a range, such as height. A phone number is categorical, because we never add two of them. The kind of data decides which average and which graph we can use.
We often cannot ask everyone, so we ask a sample of the population. A fair sample is chosen by chance, so that everyone has the same chance of being asked. A sample chosen in a convenient way, for example from one place or one club, can be biased and give a wrong picture. A fair question does not push the answer.
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