week 2

• Descriptive Statistics are measures of central tendency and measures of spread/dispersion.
• The mean measures the average score.
• The median tells us the middle score when the data are put in numerical order: 1 3 4 4 4 5 5 9 14.
• Where there is an even number of scores, the interval between the two middle scores is divided in half
• The mode tells us the most frequent score in the data: 1 3 4 4 4 5 5 9 14
• Measures of Central Tendency are used for normal distributions.
• The median is generally used for skewed distributions to derive at central tendency since it is much more robust and sensible.
• The mean is not a robust tool since it is largely influenced by outliers.
• The median is better suited for skewed distributions to derive at central tendency since it is much more robust and sensible.
• The mean is used for normal distributions.
• The median is generally used for skewed distributions.
• Confidence intervals allow us to place reasonable ranges on population estimates.
• In terms of numbers, a Sig value greater than 0.05 indicates that the distribution is not significantly different from a normal distribution.
• When generating normality statistics in SPSS, the Shapiro-Wilk statistic is used.
• A 95% confidence interval indicates a 95% chance that the population mean will fall within the range of values.
• Given that a set of scores are normally distributed, the likelihood of obtaining a score below the mean, above the mean, between the mean and 1 std dev above the mean, between the mean and +/- 1 std dev from the mean, and a score of greater than 1 std dev above the mean can be calculated.
• Measures of Central Tendency are not relevant to the data set since they are not robust tools.
• The mean is the arithmetic average of a set of numbers, or distribution.
• The median is the numeric value separating the higher half of a sample, a population, or a probability distribution, from the lower half.
• With a bell-shaped curve indicating a normal distribution, the majority of the scores lie around the central value.
• The central limit theorem proved by Ronald Fisher showed the generality of the normal distribution.
• A low standard deviation implies that the data points tend to be close to the mean.
• A perfect distribution is when scores are distributed symmetrically around the centre of the scores.
• Standard Scores (Z Scores) are used to express raw-scores in terms of their standard deviation from the mean.
• Standard deviation is a measure that is used to quantify the amount of variation or spread of a set of data values.
• The normal distribution allows us to calculate the probability of a particular score being obtained.
• Many psychological variables are distributed approximately normally, including most professional psychologists who are required to administer and interpret psychological measures.
• A normal distribution is symmetrical, has a mean, median, and mode that are all equal, and has 'tails' that never quite reach zero.
• Standard Scores (Z Scores) can be calculated using the formula:
• In order to make use of this property of the normal curve, it is important that we express a score in relation to its standard deviation from the mean – not as its raw score.
• The frequency of the scores decline the further you get from the centre.
• Using the normal distribution, we can calculate the likelihood of someone in a normally distributed population obtaining a certain score.
• A high standard deviation implies that the data points are spread out.