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Age group voting patterns in the last presidential election would be an example of nominal data. Take the age groups and describe if they tended to vote Democrat, Republican, Independent, and so on. Another example is take the same age groups and determine the brand of cars they typically purchase such as GM, Ford, Toyota, Kia and so forth. Ordinal data example would be to take the same age groups as above and determine highest percentage of education level (say from the Department of Education statistics) from the group such as High School, Technical School, Bachelor Degree, Master Degree, or Doctorate Degree (MD, PhD). Or, take the age same groups and ask then how they think Obama is doing as president: Good, Neutral, or Poor.
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The pie graph is used for what kind of data?
Any kind of data - nominal, ordinal or interval - provided you can group the data into a few categories. Ideally not more than seven, though in exceptional cases, up to ten may be used.
What is the appropriate measure of variability for ordinal data?
This is a surprisingly difficult question, partly perhaps because of the ambiguous term 'ordinal'. For instance, horse-race finishes are ordinal--horses usually finish first, second, etc., with no ties; pure order data gives no information about gaps between horses. For such data--the purest form of ordinal data--talk about variability is meaningless. You need data with 'ties' or repeated 'values'--more cases than ordered categories--to talk about variability meaningfully. If you do have repeated values, one option is to fall back and use nominal variability measures--the Index of Qualitative Variation is one; information statistics also work; and there's always the frequency/percentage table. They don't 'measure' concentration along the categoric order, obviously. Disappointingly many websites recommend using the range or interquartile range, presumably calculated by assigning numbers to the ordered categories and subtracting. These indices are very dangerous if you assume only qualitative order among categories. This is obviously flawed--if you don't know how far categories are separated, subtracting numbers is flat invalid. For instance, rank states in the US by size--Alaska is 1, RI is 50--and consider the fact that a group from AK, TX, and CA has a range of 2 and a group from NJ and MA has range of 3 [47 - 44]. First, those numbers are really meaningless; second, they sure misrepresent relations among state size differences. Unless you trust that your 'ordinal' categories are pretty close to equal intervals apart--what we call 'quasi-interval'--you simply cannot use range validly to measure ordinal variability. The same reasoning applies to inter-quartile range. You might as well use variance, since describing 'skew' and 'outliers' for ordinal data is very dangerous, itself. More valid ordinal measures do exist--I cannot recall them. But when you choose an index, take care to examine how it is treating the numbers or other ordering symbols it trades on. Invalidity is rife.
What is confidence trickery?
An attempt to defraud a person or group by gaining their confidence
What does being bias mean?
It means leaning towards a certain group person or idea.
In both elective and merit selection systems a person from which group is the most likely to be selected?
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