Learn more about creating confidence intervals that are higher (or lower) than 95%.
- For some people, 95% just isn't good enough.…So what happens if someone demands…a 98% confidence interval?…Well, let's remember a 95% confidence interval…stretches in equal distances in opposite directions…from our sample proportion.…How far?…Enough to include 95% of the probability distribution,…which means that a 98% confidence interval…would have to stretch a little bit farther…so our interval would include 98%…of the probability distribution.…
Notice my numbers didn't really get any better.…It's more like saying, "I'm 75% sure…"my lost car keys are in my living room,…"but I'm 99% sure my lost car keys…"are somewhere in this house.…I simply increase the likely location of my keys…and that increased the likelihood that this area…contained my keys.…So, when someone demands that we provide…a 98% confidence interval instead of…a confidence interval of 95%, it's important…that they understand what the difference is…between the two intervals.…
With that in mind, let's go ahead and figure out…how to calculate the limits of this expanded interval.…
Eddie Davila first provides a bridge from Part 1, reviewing introductory concepts such as data and probability, and then moves into the topics of sampling, random samples, sample sizes, sampling error and trustworthiness, the central unit theorem, t-distribution, confidence intervals (including explaining unexpected outcomes), and hypothesis testing. This course is a must for those working in data science, business, and business analytics—or anyone else who wants to go beyond means and medians and gain a deeper understanding of how statistics work in the real world.
- List the three primary issues addressed in Statistics Foundations: 2.
- Recognize two key characteristics associated with simple random samples.
- Apply the Central Limit Theorem to find the average of sample means.
- Analyze random samples during hypothesis testing.
- Assess individual situations to determine whether a one-tailed or two-tailed test is necessary.
- Define acceptance sampling.