Question

Does a margin of error imply that the research is not reliable? If an entire population is not surveyed, but rather just sampled, will results ever be 100% accurate?

Lastly, what does confidence level mean?

Answer 1

1) Margin of Error:

In confidence interval, the range of values above and below of the sample Statistic is known as Margin of Error.

In other words,the margin of error allows us to feel confident certain percentage of time within a range below and above the ideal guess, represented by a margin we believe least in error.

Margin of Error tells us that how many percentage of your estimate is actually different from your real population.

The idea behind margins of error is that any survey or poll will differ from the true value by a certain amount. However, margins of error reflect the fact that there is some for error, so although 95% or 98% confidence with a 2 percent Margin of Error might sound like a very good statistic, room for error is built in, which means sometimes statistics are wrong.

In research Statistic aren't always right, so it doesn't implies that researchers are always reliable.

2) In Statistics sampling is the one of the most important part.

If the entire population is surveyed or census survey is done then our result is 100% accurate.

But sometimes doing the census survey or sampling the whole population the not easy it requires more time and more cost as well as man power. that's why we doing the sampling .

The result of sampling is not 100% accurate we couldn't say the accurate result. It depends on the sample size.

If the sample size is large then we get the more accurate resut not 100%. Just near to accurate. If we are doing sampling results are never ever 100% accurate . If we do the census survey then results are 100% accurate.

3) Confidence level:

The conference level is the percentage of all possible samples that can expected to include true parameter.

Here the conference level is random. we can say that 95% of time the conference level can occupie the true parameter.

In other words,  A 95% confidence level implies that 95% of the confidence intervals would include the true population parameter.

Confidence levels are expressed as a percentage (for eg: 95% confidence level). It means that should you repeat an experiment or survey over and over again, 95 percent of the time your results will match the results you get from a population. Confidence intervals are your results…usually numbers.

For example,

you survey a group of animal owners to see how many cans of cat food they purchase a year. You test your statistics at the 95 percent confidence level and get a confidence interval of (200,300). That means you think they buy between 200 and 300 cans a year. You’re super confident that your results are sound, statistically significance.

Hope your concept is clear .

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Thank you.

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