BIOM301 Exam 3 True/False
Decreasing variability in population will cause length of CI to
Decrease
As sample size increases, the standard deviation of the population sampled will decrease
False
Beta represents probability of making a Type 1 error at end of study
False
Beta represents the probability that you made a type 1 error at end of study
False
Compare 95% CI for samples of size 10 and samples of size 30; increasing sample size leads to a larger percent of intervals that successfully include the true population mean
False
Decreasing sample size decreases length of a confidence interval
False
Histograms of all sampling distributions of sample means will be symmetrical
False
If our decision in a hypothesis test is to fail to reject the null, then we know the null must be true
False
If the p-value is less than the level of significance (alpha), then the decision must be not to reject null
False
If you reject the null hypothesis, the probability of a Type II error is less than alpha
False
If your test statistic falls in the critical region you will accept the null hypothesis
False
Sampling distribution of sample means will vary each time it is created
False
Statistical power of the test is the ability to reject the null hyp. when the null is really true in the population
False
only the t and not the z distribution varies with sample size
False
For n<100, the t-distribution will be more peaked and less spread out than the z-distribution
False (less peaked, more spread when n<100)
The underlying assumption of normality for a 1 pop z test for percent is met if your sample size is >30
False (n*p)>5
Underlying assumption for normality for a 1 pop. z test for percent is met if your sample size is >30
False (n*p>5)
If population is not normally distributed, the sampling distribution of sample means will appear normal if the sample size used is at least
Greater than 30
Decreasing the sample size will cause the length of the confidence interval to
Increase
Decreasing sample mean will cause CI to
Not change
Alpha represents the:
Probability of a Type 1 error
Difference between Point estimates and Interval estimates
Pt. Est.: 1 # that's the best guess of a population parameter (I.e. Sample mean) Int. Est. : range of values that could include population parameter (i.e. CI)
What value is always located at the center of a confidence interval for population mean?
Sample mean
For a 95% confidence interval, what are you 95% confident about?
That the true population parameter is located in the interval
All else equal, a 95% CI will have a larger interval length compared to a 90% CI
True
As sample size increases, a randomly selected sample will have a sample mean that is closer to the true population value
True
Both t and z distributions are always unimodal and symmetric
True
Correct decision we usually hope to make is a Type B because it represents a statistically significant outcome
True
If population is normally distributed, then the sampling distribution of sample means will also always be normally distributed
True
If we reject the null, the probability of a type one error is less than 5 percent (p< .05)
True
If you reject the null hypothesis you have a statistically significant result
True
If you reject the null you have a statistically significant result
True
Increasing sample mean will not change length of a confidence interval
True
Increasing your sample size increases the probability that the sampling distribution of sample means will be approximately normally distributed
True
Increasing your sample size results in sample means being closer to the true population mean
True
Population mean is equal to the mean of the SDSM even if population is not normally distributed
True
Population mean is equal to the mean of the sampling distribution of sample means even if the population is not normally distributed
True
Sample mean is a point estimate for population mean
True
Samples will have mean values that vary from the true population mean due to chance
True
Standard error is exactly equal to standard deviation of the sampling distribution of sample means
True
The risk of a Type 1 error is directly controlled in a hypothesis test by establishment a level for alpha
True
The standard error is equal to the std. dev. of the SDSM
True
Variability of sample mean values is estimated by the 'standard error'
True
When sample sizes are small, the t-distribution changes to require a greater level of statistical proof before you can reject null hyp.
True
As sample size increases, the SDSM will become more peaked and less spread out
True (= more normal)
Even if your t* value is in the t crit region, it is still possible that the null hypothesis is really true in the population
True (error)
You test the null hyp. that a ear infections as a child do not affect later hearing ability; you end up rejecting null hyp.; you could have made a
Type 1 error (rejecting null = possibility of type 1 error)
You have failed to reject the null when it is false, you have made a
Type 2 error
