Random Sampling Concepts

Selecting individuals from a population where each member has an equal chance of being chosen.

A sampling method where there is a systematic pattern in the selection, not randomness.

A tendency to favor some individuals over others, even without conscious intention.

A tool that contains a sequence of numbers from 0 to 9 where each digit appears with a similar frequency.

A method of selecting samples that ensures each member of the population has an equal chance of selection using random number tables.

Quota sampling involves selecting participants based on pre-defined categories (quotas) to ensure representation, but the selection within those categories is often subjective.

In quota sampling, the interviewer has the freedom to select individuals within the quotas, potentially leading to biased choices.

Quota samples can be difficult to verify since the selection process is subjective. It's hard to assess if the sample truly represents the population.

Random sampling ensures every element in the population has an equal chance of being selected for the sample.

The theory behind statistical sampling relies on the idea that samples are chosen randomly, ensuring representativeness.

In simple random sampling, every element in the population has the same probability of being selected for the sample.

Simple random sampling guarantees that all possible samples have an equal chance of being selected.

When every member of the population has the same probability of being chosen.

The act of generating numbers where each digit appears unpredictably.

The number of times a specific category appears in a dataset.

Representing data in the form of a table that shows the frequency of each category.

A way to visualize qualitative data where a circle is divided into parts representing each categories proportion in the dataset.

The proportion or percentage of the total number of observations that fall within a specific category.

Representing data in the form of a table that shows the relative frequency of each category, expressed as percentages.

Data that can be categorized and doesn't involve numerical measurements.

Data that involves numerical measurements.

A variable that can be classified into distinct categories.

A chart that presents information as grouped bars, each bar representing a different data point. The lengths of the bars are proportional to the values they represent.

A type of bar chart where each bar is divided into segments representing components of a whole. The segments add up to the total value of the bar.

Data that shows the total amount of goods or services imported into a country over a given period.

Data that shows the total amount of goods or services exported from a country over a given period.

In a component bar chart, the total height of each bar represents the sum of its components. For example, the total number of male students would be represented by the height of a bar, and the bar would be divided into two segments: Urdu medium and English medium.

In a multiple bar chart, the bars stand side by side, each representing a different category. The heights of the bars show the values for each category. However, the values in a multiple bar chart do not add up to a single total.

A chart that allows for comparing different categories or variables over time. Each group of bars represents a different category, and the bars within each group show the values for that category over different time periods.

The ability to easily compare the values of different categories or variables. This is a major advantage of using multiple bar charts to present data.

The upper limit of a class interval is the maximum value that a data point can have to be included in that class.

The lower limit of a class interval is the minimum value that a data point can have to be included in that class.

The difference between the upper and lower class limits of a class interval is the width of the interval. It's also referred to as the class size or the class breadth.

The midpoint of a class interval can help represent the entire class. It is calculated by averaging the upper and lower limits of the class interval.

Class boundaries are used to prevent gaps between consecutive classes. They are calculated by adding the upper and lower class limits of adjacent intervals and dividing by 2.

When constructing class intervals, the upper class limit of one class should be equal to the lower class limit of the next class. This ensures no gaps occur between classes.

If we use whole numbers for the upper class limits, it can create problems when assigning data points to specific classes. For example, a value of 33 might be counted in both the 30-33 and 33-36 intervals.