Statistical Techniques

What is the purpose of statistical techniques? What are their practical applications? What is there value? What advantages/ disadvantages do they have?

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  • Statistical Techniques
    • Measures of Central Tendancies (Mean, Mode, Median)
      • Purpose
        • Summarising data
        • Comparing Data
      • Application
        • Comparing pebble sizes on two stretches of beach
      • Comments
        • Enables us to summarise a data set, giving the middle value or the most frequently occurring.
        • The MEAN is particularly useful if the data has a small range
          • BUT if the range is large, the mean is likely to be heavily  influenced by extreme values which could give a distorted picture.
        • The MODE is of no value if there are no repeating values
          • There may be than one mode e.g. BI-MODAL
        • The MEDIAN is not influenced by extreme values
    • Range
      • Purpose
        • The dispersion and variability of data
        • Allows to identify data in more depth
      • Application
        • Giving the difference between lowest and highest values in a data set e.g marks in a geography test.
      • Comment
        • Gives a basic idea of the spread of data.
        • Affected by extreme values
        • Anomolies can lead to a false picture
    • Inter-Quartile Range
      • Purpose
        • Dispersion and Variability of Data
        • In-depth analysis of data
      • Application
        • Shows middle 50% values
      • Comment
        • More useful than the range in indicating the spread of data as it excludes outliers
    • Standard Deviation
      • Purpose
        • Analyse dispersion and reliability of data
        • Measure of the degree of dispersion
      • Application
        • Working out the standard deviation of bedload across a stream.
      • Comment
        • Simply another way of examining the spread of data
        • Lets you know reliabilty of mean
    • Spearmans Rank
      • Purpose
        • Shows correlation of two sets of data and their statistical significance
        • Test of strength of relationships
      • Application
        • Showing the relationship between distance inland and number of species in a psammosere
      • Comment
        • Good to analyse a scattergraph (not speculative, unlike a best fit line)
        • Enables you to demostrate a clear relationship between two data sets
        • Correlation does not mean causation
        • Only really works with 10 - 30 data sets
    • Chi-Square
      • Purpose
        • The degree to which there are differences between observed and expected data
      • Application
        • Investigating spatial disrabutions e.g. plants types at different stages of a succession.
      • Comment
        • Doesn't expalain patterns in distrabutions
        • Can test significance/ test categories/ distrabutions
        • Amount of observed data must be between 4 and 20


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