Statistical Skills

  • Created by: 8cburton
  • Created on: 02-06-15 16:53

Measures of central tendancy- Mean, median and mod


  • Simple and easy 
  • Mode can be used with non numerical data
  • Median- very large and very small numbers do not affect result
  • Mean- useful in making measurements more accurate


  • Cant use discontinuous data
  • Median and mode do not account for whole set of data
  • Mean is easily disorted by very large/small anomalies
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Interquartile range

  • Interquatile range is the spread of values around the median
  • Find out the LQ and the HQ and the difference is the IQR


  • Not affected by the outliers


  • Not all data considered
  • Complicated to work out
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Standard deviation

  • the average amount by which the values in a data set vary from the mean 
  • Calculate the mean and minus it from X
  • Square each of the answers and add up total
  • Then divide by n and square root 
  • Low standard deviation means little range and therefore reliable mean


  • More reliable measure of dispersal as it uses all the data


  • Can be greatly affected by outliers
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Spearmans rank

  • Formulate a null hypothesis.
  • Individually rank the values of each variable. 1 = highest value.
  • Find the difference between the two.
  • Square the differences and sum the values.
  • Input into the formula.


  • Indicates the statistical significance of a result - rules out chance.
  • Gives numerical value to the strength and direction of a correlation.



  • Does not show if there is a casual link
  • Too many tied ranks affect the validity of the test.
  • Subject to human error.
  • Only appropriate for data with 10-30 values with 2 variables that are believed to be related
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Mann Whitney U

  • Select null hypothesis.
  • Rank the data sets across the two columns. 1 = lowest value.
  • Treat as two seperate columns. Add ranks in first column to get your R1 value then add ranks in the second column to get your R2  value.
  • Input int the formula.
  • Choose the smaller U value of either U1 or U2.
  • Compare to the critical values table: less than the critical value means you should reject the null hypothesis at 95% confident. Greater than the critical value - accpet the nul. 


  • Used to show if there is a statistical difference between two sets of data e.g. size of rocks in upper course and lower course.


  • Does not explain cause and effect.
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Chi square

  • Identify null hypothesis - no significant difference between observed an expected.
  • Subtract observed frequencies from expected and square the result.
  • Divide this by the expected value for that group.
  • Compare with degrees of freedom: on the critical values chart, the degree will be one less than the total number of observed values.


  • To assess the degree of difference between observed and theoretical data e.g. number of pebbles along a river. 
  • Statistical significance of results can be tested.


  • Doesn't explain why there is a pattern.
  • Does not give the strenght of the relationship.
  • Percentages cannot be used.
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