Means

Non-sampling Error

Sampling Error

Type I Error

Type II Error

It lies outside essentially all previously observed values within similar studies.

It falls within one standard deviation from other observed values in similar studies.

It matches closely with more than half of other observed values in previous researches.

It shows no significant statistical difference from other previously obtained means.

The sample was chosen without any specific criteria.

The sample was chosen randomly to mirror the population.

The sample was collected over a long period of time.

The sample was selected from a large population.

Z-test statistic

Pooled variance t-test statistic

Unpooled variance t-test statistic

Chi-square test statistic

Variability has no impact on determining which test statistic to use.

Lower variability always ensures that the null hypothesis will be rejected.

A higher variability may increase the standard error and widen the confidence interval.

Increased variability reduces the likelihood of Type I errors.

Decreases type I errors but increases statistical power.

Increases type I errors but has no effect on statistical power.

Both type I errors remaining unchanged, and statistical power remains the same as well.

Increases type I errors and also increases statistical power.

Both populations have identical standard deviations.

The sample mean equals some known value.

The population mean is less than or greater than some value.

There are no differences between groups being tested.

Two-tailed test

One-tailed test

Independent samples t-test

Paired samples t-test

The alternative states that the parameter is simply not equal to a specific value ($\mu \neq \mu_{0}$)

Alternative hypotheses for two-tailed tests are never expressed in terms of inequality ($\leq$ or $\geq$)

In the two-tailed test, the alternative hypothesis states the parameter is smaller than a value ($\mu < \mu_{0}$)

The two-tailed alternative hypothesis argues the parameter is larger than a value ($\mu > \mu_{0}$)

Population is given to be approximately normal.

Distribution of sample data looks approximately symmetric with no apparent outliers or gaps.

All the answer choices

Central Limit Theorem (sample size is at least 30)