Sampling Distributions

It decreases compared to when sample sizes are equal.

There is no effect on the standard error.

It increases compared to when sample sizes are equal.

The effect on the standard error depends on the actual mean values, not sample sizes.

The pooled t-test.

The z-test with pooled variance.

The paired t-test.

The Welch's t-test.

p

$\mu_1 - \mu_2$

$p_1 - p_2$

$\sigma$

Random-digit dialing

Cluster sampling

Proportional stratified sampling

Stratified sampling

Power is not affected by within-group variability changes as it only pertains to between-group differences.

Power increases due to greater discernibility provided by wider data spread within groups making real differences clearer.

Within-group variability has an inverse square root relationship with power affecting its magnitude proportionally less with larger variances.

Power decreases because increased variability inflates the standard errors and makes detecting a true difference harder.

One sample size must be more than twice as large as another.

Both populations have equal variances.

At least one population is not normally distributed.

Both populations are normally distributed or both sample sizes are large.

Fluctuates unpredictably as influences from failing adjustments affect various test conditions differently regarding sensitivity and specificity trade-offs.

It remains unchanged while increasing Type I errors because Type II errors depend on non-rejection regions which unadjusted thresholds do not alter.

Decreases dramatically since failure to adjust thresholds raises both types of errors uniformly across comparisons.

Increases due mainly because non-adjustment leads directly more false negatives across multiple tests given constant overall alpha level.

Convenience sampling

Cluster sampling

Systematic sampling

Stratified sampling

180 scoops.

200 scoops.

210 scoops.

190 scoops.

Skewness

Covariance

Kurtosis

Variability