display of quantitive data + data distributions

• what graph is this ?

  A) line graph

  
• what graph is this?

  A) histogram

  
• what graph is this ?

  A) bar chart

  
• display of quantitative data: graphs and tables should be simple so they can be read easily
• display of quantitative data: they should clearly show the findings from a study
• display of quantitative data: there should be a short informative title
• display of quantitative data: in a graph both axes should be clearly labelled, the x-axis goes across the page, in the case of a bar chart or histogram it is usually the IV the vertical or y-axis is usually frequency
• display of quantitative data: always use squared paper if you are hand-drawing graphs
• tables: the measurements collected in a research study are referred to as 'raw data' - numbers that haven't been before any descriptive statistics have been carried out
• tables: these data can be set out in a table and/or summarised using measures of central tendency and dispersion, such summary tables are more helpful for interpreting findings
• bar chart: the height of each bar represent the frequency of each item, Bar charts are especially suitable for data that is not continuous i.e. has no particular order which is categorical or nominal data, in a bar chart a space is left between each bar to indicate the lack of continuity
• histogram: a histogram is similar to a bar chart except that the area within the bars must be proportional to the frequencies represented, in practice this means that the vertical axis (frequency) must start at 0
• histogram: in addition the horizontal axis must be continuous (therefore you can't draw a histogram w/ data in categories), finally there should be no gaps between the bars
• line graph: a line graph like a histogram has continuous data on the x-axis and there is a dot to mark the middle top of where each bar would be and each dot is connected by a line
• scattergram: a scattergram is a kind of graph used when doing a correlational analysis
• data distributions: when we plot frequency data the y-axis represents frequency and the x-axis is the item of interest as in a histogram, when doing this for large data sets we can see an overall pattern of the data called a distribution
• normal distribution: the normal distribution is a classic bell-shaped curve it is the predicted distribution when considering an equally likely set of results
• normal distribution: e.g. if a light bulb has a mean lifetime of 100 hours we would expect some light bulbs to last a little less than this and some to last a little more
• normal distribution: if we plot a lifetime of 1,000 light bulbs we would get a normal distribution, many human characteristics are normally distributed such as shoe sizes or intelligence, a normal distribution has certain defining features
• normal distribution: the mean, median and mode are all in the exact mid-point
• normal distribution: the distribution is symmetrical around this mid-point
• normal distribution: the dispersion of scores of measurements either side of the mid-point is consistent and can be expressed in standard deviations
• normal distribution: for any set of data that is normally distributed, 34.13% of the people will lie within one standard deviation below the mean and 34.13% will lie within one standard deviation above the mean
• normal distribution: therefore a total of 68.26% will lie within one standard deviation above or below the mean, a total of 95.44% of people lie two standard deviations above or below the mean which means that only 4.56% lie in the area beyond this, 2.28% are less than two standard deviations below the mean
• normal distribution: this concept underlies the idea of the statistical deviation model of abnormality, people in the sections above or below two standard deviations are very unusual indeed
• skewed distribution: in some populations scores are not distributed equally around the mean - this is skewed distribution, consider a test of depression where 0-50 represents normal behaviour and 50+ represents clinical depression
• skewed distribution: if we plotted the distribution of scores for 100 people we would expect most scores to be towards the low end rather than the high end of this score range, this produces a positive skewed distribution
• skewed distribution: the fact that there are a few extreme high scores has a strong effect on the mean, which is always higher than the median and mode in a positive skew
• skewed distribution: the alternative is a negative skewed distribution, this might happen if marks were plotted for an exam which was very easy so most people got a very high score
• what is this?

  A) right skewed (positive skewness)

  
• what is this ?

  A) left skewed (negative skewness)

  
• what is this ?

  A) 0.13%

  B) 13.59%

  C) 2.15%

  D) 34.13%