stats

• descriptive statistics = Analysis of data that helps describe, show or summarize data in a meaningful way
• measure of dispersion = shows how a set of data is spread out, examples are the range and the standard deviation
• range = distance between the lowest and the highest value in a set of scores.

  strength = Easy to calculate

   

  Limitation = Very affected by extreme values
• standard deviation = the average spread of scores around the mean - greater the standard deviation the more spread out the scores are

  
  

  strength = Precise measure - takes into account all of the data points

  
  

  limitation = Harder to calculate
• measures of central tendency = measurement of data that indicates where the middle of the information lies
• mean = calculated by adding all the scores in a set of data together and dividing by the total number of scores

  strength = the most 'sensitive' as it represents every single score + distance between them

  limitation = can be skewed by outliers
• median = calculated by arranging scores in a set of data from lowest to highest and finding the middle score

  strength = isn't affected by outliers

  limitation = does not represent all data points
• mode = most common data point

  strength = easy to calculate

  limitation = useless if there's more than 1 mode
• level of data = used to describe information within the values.
• nominal data = data that is not measured on a scale + cannot usually be ordered, such as gender and hair colour
• ordinal data = data that can be put in order, age, height etc
• interval data = data that is measured in terms of a fixed interval, such as height or weight
• normal distribution = arrangement of a data that is symmetrical + forms a bell shaped pattern where the mean, median and mode all fall in the centre at the highest peak.

  
• negative skew = the mode is a higher value than the mean and median, skew is to the left

  
• positive skew = skewed to the right, the mean will be greater than the median

  
• histogram = graph that is used for continuous data (e.g. test scores), should be no space between the bars, because the data is continuous.
• bar chart = graph that shows the data in the form of categories (e.g. behaviours observed) that the researcher wishes to compare.
• inferential statistics =  ways of analysing data using statistical tests that allow the researcher to make conclusions about whether a hypothesis was supported by the results.
• type 1 error = a false positive, where you accept the alternative/experimental hypothesis when it is false.
• type 2 error = false negative, it is where you accept the null hypothesis when it is false
• level of significance = level of significance is the measurement of the statistical significance, defines whether the null hypothesis is assumed to be accepted or rejected
• p≤ = 0.05, 0.1 or 0.01 = which means that the probability of chance factors affecting the result is 5%, 10% or 1% (respectively) or less.
• sign test = statistical test used to analyse the direction of differences of scores between the same or matched pairs of subjects under two experimental conditions
• inferential stats table

  A) test of difference

  B) independent groups

  C) repeated measures

  D) correlation

  E) nominal

  F) ordinal

  G) interval

  H) Chi2

  I) sign test

  J) Chi2

  K) Mann Whitney

  L) Wilcoxon

  M) Spearman's rho

  N) Unrelated t test

  O) related t test

  P) Pearson's r