The word ginormous was added to the Merriam-Webster Dictionary a few years ago. AOL conducted an online poll in which internet users were asked “What do you think of the word ‘ginormous’?” Among the internet users who chose to respond, 12,908 gave the word the thumbs up while 12,224 other users gave it a thumbs down. What do these results tell us about how the general population feels about the word ginormous? What methods of statistics can be used with the sample data for inferences about the general population? Explain.
The correct answer and explanation is :
Correct Answer:
The results from the AOL online poll indicate that among the users who chose to respond, a slightly larger number (12,908) approved of the word “ginormous” than those who disapproved (12,224). However, these results cannot be generalized to the entire population because the sample is not representative. The data come from a self-selected sample of internet users, introducing selection bias.
Explanation (Approx. 300 Words):
To understand what the general population thinks about the word “ginormous,” it’s essential to use data that accurately represents that population. In the case of the AOL poll, the sample is composed only of internet users who visited the site and chose to participate. This is a non-random, voluntary response sample, which introduces two major issues: non-representativeness and self-selection bias.
First, the sample is limited to AOL users with internet access, excluding segments of the population who may not use the internet frequently or visit AOL. Second, those who responded to the poll likely had stronger opinions about the word than those who ignored it, skewing the results.
As a result, we cannot draw valid conclusions about how the general population feels about the word “ginormous” from this data. The small difference in votes (less than 3% between thumbs up and thumbs down) is statistically meaningless when the sample is biased.
To make valid inferences about the general population, inferential statistics should be applied to data from a random sample. This includes methods such as:
- Confidence intervals to estimate the proportion of people who like the word.
- Hypothesis testing to determine if a significant majority supports or opposes the word.
- Sampling distributions to assess variability and reliability of the sample statistics.
These inferential methods require a random and representative sample to ensure the findings can be generalized to the broader population with known levels of confidence and error.