Untitled Quiz

The number of times a variable has taken values equal to or less than a given value.

The difference between the highest and lowest values in a dataset, expressed as a percentage of the initial value.

A bar graph used to display the distribution of a continuous quantitative variable.

A line graph that shows the trend of data in an istogramma.

A variable that can take on any value within a given range.

A bar graph used to represent a discrete quantitative variable.

A circle divided into segments to show the proportion of different categories in a dataset (qualitative variable).

A variable that categorizes data into distinct groups with no inherent order.

The value that appears most frequently in a dataset.

The middle value in an ordered dataset. Half the values are lower, half are higher.

Values that divide a dataset into specific portions (e.g., quartiles, percentiles).

The value below which a given percentage of the data falls.

The extent to which data points in a dataset differ from one another.

A measure of how spread out numerical data is.

Data is equally distributed around the mean.

Data is not evenly distributed; most values cluster on one side of the mean.

Calculated as the ratio of favorable outcomes to the total number of possible outcomes in an experiment.

Estimated by observing how often an event occurs over many repetitions of an experiment.

An estimate based on available information and expert judgment; not statistical.

A single outcome of a random phenomenon. It can't be broken down further.

Combination of two or more simple events.

Set of all possible outcomes of a random experiment.

The probability of the occurrence of either of two or more mutually exclusive events equals the sum of their individual probabilities.

Events that cannot occur at the same time in a single trial.

Systematic errors occurring during data acquisition.

Differences in data collection methods and classification criteria.

Using mortality data instead of morbidity data might hide true disease rate improvements due to better healthcare.

Studies aiming to find disease causes and factors influencing its development and spread.

Observational studies collecting data at a single point in time to assess the presence of conditions in a population.

Measurement of the presence of a condition in a population at a specific time.

Estimating prevalence, risk factor distribution, biological characteristics, health trends, service demand, and generating disease-factor hypothesis.

Steps for conducting a cross-sectional study including objective definition, model evaluation, literature review, participant selection, data collection methods, data presentation, and statistical analysis.

A statistical method to evaluate claims or hypotheses about a population parameter.

A type of data collection where each data point is related to another, either by matching subjects or through a before-and-after comparison on the same subject.

A statistical test used to assess if there's a relationship between categorical or dichotomous variables, by comparing observed and expected frequencies.

The initial assumption that there's no significant difference or effect.

The actual frequencies that are found in a given dataset.

The hypothesis that states there is a significant difference or effect.

The frequencies that would be predicted if there was no relationship between the variables being studied.

The probability of rejecting a true null hypothesis. Commonly 5%.

The theoretical basis for the chi-square test; assumes no link between the variables being compared.

Values of a test statistic that lead to rejection of the null hypothesis.

A way to calculate the difference between observed and expected frequencies, to see if there's a relationship. (Σ (O-E)²/E).

Values of a test statistic that lead to acceptance of the null hypothesis.

Variables that can be divided into groups or categories, not meaningful numerical values.

Rejecting a true null hypothesis.

Failing to reject a false null hypothesis.

Calculations made to determine the expected frequencies in case of no effect or relationship between the categories being analyzed.