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[Klausen]

Olga Graf

[Datasmurf]

AmBeam - Alone In The Universe

[Tkse]

Arizala or Riascos?

[Datasmurf]

Tror nok at de kommer til å få det vanskeli nå som amfi har sikra seg de beste butikkene.

[Kea]

Dummaste setningen eg ever har skrevet

[*F*]

http://www.tlc.com/tv-shows/extreme-couponing

[Datasmurf]

Bassic - Omniom Odone

[kthjorpthjopøri]

[Chocolate-Monster]

http://open.spotify.com/track/7K1AWXb1n65uieyzeCRI5Z

[Gjest Slettet-x7D6du0Hjb]

[War]

Visual Basic

[JokerN_]

https://www.finn.no/finn/boat/used/object?finnkode=46266146&serachclickthrough=true

[Datasmurf]

Mangler bare Aids

[ærligøs]

Biased Sample

 

Biased Statistics

Loaded Sample

Prejudiced Statistics

Prejudiced Sample

Loaded Statistics

Biased Induction

Biased Generalization

 

1. Sample S, which is biased, is taken from population P

2. Conclusion C is drawn about population P based on S

 

a sample is biased or loaded when the method used to take the sample is likely to result in a sample that does not adequately represent the population from which it is drawn

 

biased samples are less likely to contain numbers proportional to the whole population

 

three types of samples that are aimed at avoiding bias:

 

1. Random Sample: nothing but chance determines which members of the population are selected for the sample. Ideally, any individual member of the population has the same chance at being selected as any other

2. Stratified Sample:

a) The relevant strata (population subgroups) are identified

b) The number of members of each stratum is determined

c) A random sample is taken from each stratum in exact proposition to its size.

 

3. Time Lapse Sample

 

bias is relative to the purpose of a sample

[Chocolate-Monster]

http://www.imdb.com/title/tt1386683/?ref_=nv_sr_1

[Datasmurf]

Titles (Main Theme From Chariots Of Fire)

[Tkse]

3xo2kdb

[Datasmurf]

Samt

[kterjesen]

http://www.tourdesfjords.no/

[Skogslik]

20577109