It would be 10,000 x 7= 70,000
You have asked quite an ordinary question, specifically related to the definition as well
as perhaps the usage of a certain 9-letter word in the English language, being the
official language of the United States of America, the United Kingdom, and several
other of the Earth's sovereign nations, and in addition, the lingua franca de facto
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'verbosity' ... a noun comprised of four syllables ... is the practice in speech or writing
of using an excess of words. Put in other words, it is the the use of a great and
usually an excessive number of words, or the habit of being wordy.
C.
<span>Readers are confused when childhood experiences are recalled by an adult.</span>
Answer:
Sample size refers to the number of observations that will be included in a statistical sample.
A sample is a collection of objects, individuals or phenomena selected from a statistical population usually by a given procedure.
The sample size affects the following:
- Confidence and Margin of Error - The more a population is varied, the higher the unreliability of the calculations or estimates. In the same vein, as the sample size increases, we have more information. The more information we have, the less we error or uncertainty we have.
- Power and Effect Size - Upping the sample size enables one to detect variances. Put differently, on the balance of probability, an average obtained on a larger sample size will exceed the average real than average collected on a smaller sample size.
- Size Versus Resources - An overtly large sample will lead to a waste of resources that are already scarce and (where human subjects are involved) could expose them unecessarily to related risks.
- A study should only be carried out only if, on the balance of probability, there is a fair chance that the study will produce useful information.
- Variableness - Population Sampling makes room for variableness. Variableness ensures that every member of the population has a probability of being represented in the sample.
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