Chi–Squares

Census

Parameter

Prediction

Statistic

Histograms display frequency distributions across intervals while pie charts show proportions for categorical data.

Pie charts cannot be used for quantitative variables at all under any circumstances.

Histograms are simpler to create as they require no calculations whatsoever.

Pie charts can only show frequencies for exactly five categories or less.

Selection bias

Measurement error

Response bias

Non-response bias

Count the number of passing grades in each class.

Determine the mode score for each class's test results.

Calculate and compare the mean test scores.

Measure the range of scores from highest to lowest in both classes.

It measures the variability of the data

It validates the original claim

It determines the statistical power of a test

It quantifies the observed difference between variables

Assuming temporal precedence by documenting occurrences happening chronologically offers enough evidence for asserting causal links.

Applying multilevel modeling techniques to analyze complex data structures potentially revealing hidden confounding variables.

Basing assumptions solely on large sample sizes, believing they automatically mitigate any influence from lurking variables.

Sticking strictly to descriptive statistics when dealing with associative findings, avoiding stepping into territory concerning cause-and-effect claims.

Levene's test provides mechanism comparing variances directly between multiple groups determining statistically significant differences exist hence suitable here detecting predicted disparity variability amid subpopulations sharing comparable means.

Bartlett’s test evaluates homogeneity variances under assumption normality involved distributions hence capable signaling distinctions formalizing further inquiry into posited elevation variability amidst certain cohorts maintaining alike average measurements.

ANOVA analyzes differences among mean scores various groups although primarily focused centers rather spread still indirectly pointing presence unequal dispersion thus requires additional examination cases suspected increased variance.

Two-sample t-tests check hypothesis regarding difference average values among distinct populations offering indirect approach evaluating potential variation discrepancy mentioned scenario necessitating further exploration nonetheless.

It only affects Type I errors but not Type II errors.

It has no effect on Type II errors whatsoever.

It increases the probability of Type II errors.

It decreases the probability of Type II errors.

Cluster Sampling

Systematic Sampling

Stratified Random Sampleing

Convenience Sampling

Zero falls within the range of most likely values for the mean.

The sample size was too small to detect any effects properly.

It is likely that the true mean differs from zero.

The standard deviation of the sample is equal to zero.