1.  What does the margin of error tell us about a sample taken from a large population?  How does the confidence level for a sample outcome differ from the sample’s margin of error?

First, the margins of error and sample size are considered an inverse relationship (Rumsey, n.d.).  Second, increasing the sample size furthermore than what you already may have will give you a reduced return since the increases accuracy will be negligible (Rumsey, n.d.).

A notion, indispensable in estimating the chances that a sample is accurate is considered the margin of error (Carey, 2011).  The population is defined as the average value of a variable, where the reference class is a population interest, and the sample can be defined as the same; however, the reference class is a sample from the population.  When taking samples from a larger population one must keep in mind that the larger the population, the greater the chances the results took will be accurate (Carey, 2011).  In lamest terms, the margin of error decreases and as the sample size increases (Carey, 2011).  The aforementioned relationship is called an inverse because both, the margin of error and sample size, move in opposite directions.

The population is gauged by two important statistics, the margin of error and confidence level.  The confidence level can be defined as the percentage of all samples, which can be expected to include the true population parameter or in lamest terms, how confident we are in a given margin of error.  Both margins of error and confidence level are closely related.  Both depend on the sample size and not the population size.  As known, with the increase of the sample size, the margin of error decreases.  However, as the population increases, the margin of error increases as well.  Lastly, as the confidence level increases so does the margin or error increases.

2.  What is a null hypothesis in causal research and what does it mean to say a study has failed to reject the null hypothesis?

A hypothesis is defined as a theory or speculation, which is based on insufficient evidence that lends it to further experimentation and testing (Gonzalez, n.d.).  However, the null hypothesis can be defined as a hypothesis, which says there is no statistical significance between the two variables in the hypothesis (Gonzalez, n.d.).  The following are a couple of examples of the null hypothesis.  Question: do teens access the Internet more than adults using a cellular phone?  Null hypothesis: age, teens or adults, has zero effect when a cellular phone is used to access the Internet.  Question:  does taking a daily dose of aspirin to reduce your chances of a heart attack?  Null hypothesis: taking a daily dose of aspirin does not reduce your chances of a heart attack.