Sampling flashcards
22 practice cards drawn from the Sampling lessons. Tap a card to turn it over. Every answer is checked against the lesson it came from.
Read the Sampling lessons in full →
Is the true proportion of faulty parts a parameter or a statistic?
parameter
from “Populations and Samples”
Is the proportion faulty in the box you opened a parameter or a statistic?
statistic
from “Populations and Samples”
A survey about exercise asks the first 20 people leaving a gym. What is wrong?
the venue decided who could be sampled
from “Sampling Methods”
A school asks every single student about lunch. What is that called?
a census
from “Sampling Methods”
Volunteers were split by coin toss. What does that give the study?
a cause-and-effect conclusion
from “Observational Studies and Experiments”
An observational study finds a new drug goes with lower blood pressure. What may you conclude?
the two are associated
from “Observational Studies and Experiments”
Suppose the tagged fish spread right through the pond before the next catch. Is the estimate sound?
sound
from “Capture and Recapture”
Suppose half the tags fall off before the next catch. Is the estimate sound?
flawed
from “Capture and Recapture”
Samples of 4 are taken from these ten scores again and again. How are the sample means spread?
more narrowly than the scores
from “The Sampling Distribution”
Samples of 3 are taken from these ten scores again and again. How are the sample means spread?
more narrowly than the scores
from “The Sampling Distribution”
The population variance is 4 and the sample size is 9. What is the variance of the sample mean?
from “Mean and Variance of X-bar”
The population variance is 25 and the sample size is 4. What is the variance of the sample mean?
from “Mean and Variance of X-bar”
Sigma is 12 and n is 4. What is the standard error?
6
from “Standard Error”
Sigma is 6 and n is 25. What is the standard error?
1.2
from “Standard Error”
As the sample size grows, what does the central limit theorem say about the population itself?
unchanged
from “The Central Limit Theorem”
As the sample size grows, what does the central limit theorem say about the sample means?
roughly normal
from “The Central Limit Theorem”
Is a sample of 5 usually enough for the normal approximation?
no
from “Applying the Theorem”
Is a sample of 30 usually enough for the normal approximation?
yes
from “Applying the Theorem”
X ~ . Which normal approximates it?
mean 30, variance 24
from “Approximating a Binomial”
X ~ . Which normal approximates it?
mean 15, variance 12
from “Approximating a Binomial”
Approximating P(X < 7), which boundary do you use?
6.5
from “Continuity Correction”
Approximating , which boundary do you use?
4.5
from “Continuity Correction”