1 / 10

The Population vs. The Sample

We will likely never know these ( population parameters —these are things that we want to know about in the population). The population Number = N Mean = m Standard deviation = s. The Population vs. The Sample. Cannot afford to measure parameters of the whole population. Types of Samples.

hayes-nguyen
Download Presentation

The Population vs. The Sample

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. We will likely never know these (population parameters—these are things that we want to know about in the population) The population Number = N Mean = m Standard deviation = s The Population vs. The Sample Cannot afford to measure parameters of the whole population

  2. Types of Samples • Haphazard sampling • Convenience or self-selection • Quota sampling • Categories and proportions in the population • Probability sampling • Random sampling • Multistage cluster sampling • accuracy (margin of error) & confidence level

  3. We will likely never know these (population parameters—these are things that we want to know about in the population) The population Number = N Mean = m Standard deviation = s The Population vs. The Sample Cannot afford to measure parameters of the whole population So we draw a random sample.

  4. The Population vs. The Sample The sampleSample size = nSample mean = xSample standard deviation = s Cannot afford to measure parameters of the whole population So we draw a random sample.

  5. Does m= x? Probably not. We need to be confident that x does a good job of representingm. The population Number = N Mean = m Standard deviation = s The Population vs. The Sample The sampleSample size = nSample mean = xSample standard deviation = s

  6. How closely does our sample mean resemble the population mean(a “population parameter” in which we are ultimately interested)? random sampling error Population parameter = sample statistic + (or “standard error”) Connecting the Population Mean to the Sample Mean Random sampling error = (variation component) .or “standard error” (sample size component) Use a square-root function of sample size The sampleSample size = nSample mean = xSample standard deviation = s s = measure of variation Standard error (OR random sampling error) = s .Ö(n-1) Population mean = x + s .Ö (n-1) The population mean likely falls within some range around the sample mean—plus or minus a standard error or so.

  7. To Compute Standard Deviation • Population standard deviation • Sample standard deviation

  8. Why Use Squared Deviations? • Why not just use differences? • Student A’s exam scores/(Stock A’s prices): • 94, 86, 94, 86 • Why not just use absolute values? • Student B’s exam scores/(Stock B’s prices): • 97, 84, 91, 88 • Which one is more spread out /unstable /risky /volatile?

  9. Real-world Data & Visualization • CIA World Factbook rankings • https://www.cia.gov/library/publications/the-world-factbook/rankorder/rankorderguide.html • Gapminder • http://www.gapminder.org/world

More Related