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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Answer:
An argument relies on a comparison of two things
Explanation:
Many arguments rely on an analogy between two or more objects, ideas, or situations. If the two things that are being compared aren’t really alike in the relevant respects, the analogy is a weak one, and the argument that relies on it commits the fallacy of weak analogy.
Analogy: a comparison between two things, typically for the purpose of explanation or clarification.
Fighting will happen soon and Caesar will not be a fit leader. What event does Casca say will take ...
B! in honor of. i hope this helps
