Review of Introductory Statistics

Statistics is the discipline that focuses on the collection, analysis, interpretation, and presentation of data. It involves organizing data into tables comprised of rows and columns, where each row represents a specific case or unit, and each column signifies a variable.

Types of Variable

When it comes to the types of data or variables we encounter in statistics, they are broadly categorized into:

1) Categorical Variable:

Categorical variable is qualitative in nature, consisting of names, labels, or descriptors that categorize or identify as an object. This type of data is further subdivided into two categories: nominal and ordinal.

a) Nominal Variable:

This subtype includes variables whose values represent categories without any inherent order. For example, names such as Harry, Jake, and Jordan are nominal, as they simply identify individuals without implying any ranking or order.

b) Ordinal Variable:

In contrast, ordinal data encompasses variables that possess a clear, meaningful order. Sizes like small, medium, and large are typical examples, where each term signifies a relative measure that can be ranked.

2)Numerical Variable:

Numerical variable, or quantitative data, are quantifiable and express a numerical quantity. They can represent counts, measurements, or any other kind of numerical value, and are classified into two types: discrete and continuous.

a) Discrete variable:

This type includes variables that take on a countable number of distinct values. Examples can be the number of books on a shelf or the number of pets in a household.

b) Continuous Variable:

Continuous data variables, on the other hand, can assume any value within a range. Measurements like height, weight, and temperature are continuous, as they can vary infinitely within their respective scales.

Population V/s Sample:

The concept of population in statistics refers to the complete set of individuals, cases, or objects about which information is sought for.

A sample is a smaller, randomly selected portion of the population used for the purpose of analysis. A sample serves as a representative subset, enabling researchers to make inferences about the population. To ensure that the sample accurately reflects the population, it is essential that the selection process is random, allowing each member of the population an equal chance of being included in the sample.

Parameter V/s Statistic:

The terms “parameter” and “statistic” refer to two distinct types of measurements. Here’s a clearer breakdown of these concepts and how they differ:


A parameter is a value that quantifies a characteristic of an entire population. It is derived from measurements of all members within the population. An example of a parameter is the mean (average) value of a population. Parameters provide a complete overview of the population’s properties but are often impractical to obtain due to the size or inaccessibility of the entire population.


A statistic, on the other hand, is a value that describes a characteristic of a sample—a subset of the population. Statistics are calculated from sample data and are used as estimates of the corresponding population parameters. Common examples of statistics include the sample mean or the sample standard deviation. Statistics are practical and commonly used due to the challenges of measuring entire populations.

To illustrate these concepts, let’s compare parameters and statistics through some key summary values:

Descriptive Statistics V/s Inferential Statistics

Descriptive Statistics:

Descriptive statistics play a crucial role in data analysis by summarizing and organizing data in a meaningful way. This approach focuses on presenting the data’s main features through various statistical measures and visual tools, making it easier to understand the underlying patterns and relationships. Descriptive statistics allow for conclusions only about the dataset at hand, not about the broader population from which the sample may have been drawn. By summarizing data graphically and through key measures, it reveals patterns and relationships within the dataset.

Key Methods in Descriptive Statistics:
  • Frequency Counts and Relative Frequency: For categorical data, summarization can begin with frequency counts, which tally how often each category appears. Relative frequency, calculated as the frequency of a specific class divided by the total number of observations, provides insight into the proportion of each category within the dataset.
  • Central Tendency, Distribution, and Variance: Descriptive statistics utilize measures of central tendency (mean, median, mode), distribution (range, interquartile range), and variance (standard deviation) to describe the dataset’s characteristics. These measures offer a snapshot of the data’s overall shape and spread.
  • Graphical Representations: Visual tools such as histograms, pie charts, and box plots effectively summarize data. Histograms, used for quantitative data, display frequencies with bars touching each other, indicating continuous data. Pie charts are best suited for displaying the proportion of categories when there are only a few distinct ones; however, they become less effective with multiple categories.

Inferential Statistics:

Inferential statistics take a step further by enabling us to make predictions, decisions, and generalizations about a population based on sample data. This branch of statistics uses the collected data to infer properties of an underlying population, extending beyond the immediate dataset.
Inferential statistics bridge the gap between sample data and the larger population, providing a foundation for making informed decisions and predictions about population parameters based on the sample analysis.

Techniques in Inferential Statistics:
  • Hypothesis Testing and Confidence Intervals: These methods assess the likelihood of a hypothesis being true for the population and estimate the range within which a population parameter is likely to lie, with a given level of confidence.
  • Regression and Correlation Analysis: These tools evaluate the relationships between variables, predicting one variable based on the value of another and measuring the strength and direction of their association.

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