The standard deviation of the sampling distribution is called:

As sample size increases, the standard error:

The standard error of the sample mean is:

The larger the sample size, the:

A statistic is:

A sampling distribution becomes normal when:

Large sample tests are applicable when:

In large samples, the test statistic for mean is based on:

For two large independent samples with means x̅₁, x̅₂ the test statistic is:

For testing population mean μ using large sample, test statistic is:

In large sample tests, Z is:

The null hypothesis in a mean test is typically:

Critical values for Z at 5% level (two-tailed) is:

If test statistic lies beyond the critical value:

A Z-value of 3.2 implies:

In large samples, if population is not normal, the sampling distribution:

If calculated Z < critical Z, we:

The standard error of difference of two means is:

A population proportion is denoted by:

For proportion tests, the standard error is:

In proportion testing, p and q stand for:

The success probability in a binomial distribution is:

In a two-proportion test, if p1 = 0.7, p2 = 0.6, n1 = 100, n2 = 100, pooled p =:

In testing large sample proportions, if p = 0.5 and n = 100, SE is: