Some key points to remember

• Distribution of scores or values can be displayed by using Box or Whiskers plots and histograms.
• Frequency and percentage can be computed for ordinal data. Eg: Strongly disagree to strongly agree.
• We can compute Frequency and percentage for interval and ratio level data as well. Eg: Age, temperature, Height, weight.
• Distribution of interval/ ratio data often forms a “bell shaped” curve.
• Measures of central tendency and measures of dispersion are computed with ratio/interval data.

Note:

a) Measure of central tendency:

• Mean, Median and Mode.

b) Measure of dispersion:

• Variance, Standard Deviation, Standard error of the mean.
• Describes how “ Spread out” a distribution score is.
• Increased variance, Scores are all over the place and don’t necessarily fall close to the mean.

Central limit theorem

• The larger the sample size, more normal the distribution of sample mean become. i.e. increased sample size α increased normal distribution.
• Helps to draw conclusion without having knowledge about the distribution of population.
• Sample size 30 or greater than 30 makes a normal distribution.

Probability

• Chance of getting selected or an event to occur.
• Occurs between 0 ( NO) to 1 ( Yes).
• Possibility of all possible events always sums to 1.
• More the number of sample means more normal the binomial distribution.