![]() ![]() You will need to decide which is more important in your analysis. ![]() These two measures are therefore often contradictory: a more robust measure is likely to be less efficient. The mean is therefore very efficient, because it uses all the data. The median is therefore more robust than the mean, because it is not affected by outliers, and grouping is likely to lead to very few changes.Įfficiency is a measure of how well the summary measure uses all the data.Ī more efficient measure uses more data. A robust measure is NOT sensitive to these changes. These changes in data quality can arise either through outliers, extreme values at either end, or from actions taken during analysis, such as grouping the data for further analysis. Robustness is a measure of how sensitive the summary measure is to changes in data quality. There are two constructs (ideas or concepts) that are commonly used to assess summary measures such as mean, median and mode. The values of mean, median and mode are not the same, which is why it is really important to be clear which ‘average’ you are talking about.Īssessing summary measures: robustness and efficiency It cannot be used for further statistical analysis. ![]() The mode is the most common value in a data set. The median is not skewed by extreme values, but it is harder to use for further statistical analysis. However, it can be skewed by ‘outliers’, values which are atypically large or small.Īs a result, researchers sometimes use the median instead. It has the advantage that it uses all the data values obtained and can be used for further statistical analysis. When most people say average, they are talking about the mean. See our page on Averages for more about calculating each one, and for a quick calculator. There are three measures of average: mean, median and mode. The average gives you information about the size of the effect of whatever you are testing, in other words, whether it is large or small. This might, for example, be ‘men’, ‘women’, and ‘other/no gender specified’, grouped by age categories 20–29, 30–39, 40–49 and 50–59. One of the most common techniques used for summarising is using graphs, particularly bar charts, which show every data point in order, or histograms, which are bar charts grouped into broader categories.Īn example is shown below, which uses three sets of data, grouped by four categories. For example, if you think you may be interested in differences by age, the first thing to do is probably to group your data in age categories, perhaps ten- or five-year chunks. The starting point is usually to group the raw data into categories, and/or to visualise it. The first thing to do with any data is to summarise it, which means to present it in a way that best tells the story. Summarising Data: Grouping and Visualising This page provides a brief summary of some of the most common techniques for summarising your data, and explains when you would use each one. There is a wide range of possible techniques that you can use. It’s now time to carry out some statistical analysis to make sense of, and draw some inferences from, your data. Once you have collected quantitative data, you will have a lot of numbers. Understanding Statistical Distributions.Area, Surface Area and Volume Reference Sheet.Simple Transformations of 2-Dimensional Shapes.Polar, Cylindrical and Spherical Coordinates.Introduction to Cartesian Coordinate Systems.Introduction to Geometry: Points, Lines and Planes.Percentage Change | Increase and Decrease.Mental Arithmetic – Basic Mental Maths Hacks.Ordering Mathematical Operations - BODMAS.Common Mathematical Symbols and Terminology. ![]() Special Numbers and Mathematical Concepts.How Good Are Your Numeracy Skills? Numeracy Quiz. ![]()
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