Statistics

1. Identify the scales of measurement for each variable
Nominal: Variables that have categories without any intrinsic order (e.g., gender, race).
Ordinal: Variables with categories that have a clear order or ranking (e.g., education level, socioeconomic status).
Interval/Ratio: Variables where the difference between values is meaningful and there is an absolute zero (e.g., temperature in Celsius, income).
For each variable in your dataset, determine which scale of measurement (nominal, ordinal, interval, or ratio) applies.

2. Identify reasonable measures of central tendency
Mean: Suitable for interval or ratio data where values are numeric and evenly distributed.
Median: Suitable for ordinal or interval/ratio data to describe central tendency without being skewed by extreme values.
Mode: Suitable for nominal, ordinal, or interval/ratio data where identifying the most common value is meaningful.
Choose the appropriate measure (mean, median, or mode) based on the scale of measurement of each variable.

3. Identify reasonable measures of variability
Range/Interquartile Range: Useful for ordinal, interval, or ratio data to show the spread between the highest and lowest values (range) or within the middle 50% of data (interquartile range).
Standard Deviation/Variance: Appropriate for interval or ratio data to quantify the dispersion around the mean.
None: Sometimes variability measures are not applicable, especially for nominal data.
Select the appropriate measure of variability (range/interquartile range, standard deviation/variance, or none) depending on the scale of measurement of each variable.

4. Identify reasonable graphic depictions
Bar/Pie: Suitable for nominal or ordinal data to represent frequencies or proportions of categories.
Histogram: Suitable for interval or ratio data to show the distribution of values.
Choose either a bar/pie chart or a histogram based on the scale of measurement of each variable to visually represent the data.