Statistical Techniques
What is the purpose of statistical techniques? What are their practical applications? What is there value? What advantages/ disadvantages do they have?
- Created by: Bethany
- Created on: 08-04-14 19:26
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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
- Purpose
- 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
- Purpose
- 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
- Purpose
- 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
- Purpose
- 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
- Purpose
- 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
- Purpose
- Measures of Central Tendancies (Mean, Mode, Median)
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