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Introduction to Statistics

Introduction to Statistics

Edited By Komal Miglani | Updated on Jul 02, 2025 07:53 PM IST

Statistics is the field focused on collecting, analyzing, interpreting, presenting, and organizing data. Essentially, it’s a branch of applied mathematics aimed at summarizing information. A key aspect of statistics involves dealing with uncertainty and variation, which are crucial for understanding different phenomena across various fields. By using statistical analysis, we can measure and interpret these uncertainties.

This Story also Contains
  1. Statistics
  2. Data
  3. Measures of Central Tendency
  4. Measures of Dispersion
  5. Representation of Data
  6. Solved Examples on Statistics
  7. Summary
Introduction to Statistics
Introduction to Statistics

This article is about the concept fundamentals of Statistics. This is an important concept which falls under the broader category of Statistics. This is not only important for board exams but also for various competitive exams.

Statistics

Statistics is a mathematical science including methods of collecting, organizing, and analyzing data so that meaningful conclusions can be drawn from them. In general, its investigations and analyses fall into two broad categories descriptive and inferential statistics.

1. Descriptive statistics deals with data processing without attempting to draw inferences. The data are presented in the form of tables and graphs. The characteristics of the data are described in simple terms. Events that are dealt with include everyday happenings such as accidents, prices of goods, business, incomes, epidemics, sports data, and population data.
2. Inferential statistics is a scientific discipline that uses mathematical tools to make forecasts and projections by analyzing the given data. This is of use to people employed in such fields as engineering, economics, biology, the social sciences, business, agriculture, and communications.

Data

Any bit of information is data. For example, the marks you obtained in your Math exam are data.

Data is a collection of information, measurements or observations. The data once collected must be arranged or organized in a way so that inferences or conclusions can be made out from it.

Data Collection Method

1. Surveys: Surveys include collecting data from some individuals.

2. Experiments: Experiments include collecting the information but under some conditions.

3. Observational Studies: It includes collecting data without manipulating

Types Of Data

1. Quantitative Data: It is the numeric data that is quantifiable.

2. Qualitative Data: It is non-numeric data that describes qualities or characteristics.

Measures of Central Tendency

A measures of central tendency (or central value) is a single value that attempts to describe a set of data by identifying the central position within that set of data. Apart from mean (often called the average), there are other central values such as the median and the mode. The valid measures of central tendency are

  • Mean
  • Median
  • Mode

Measures of Dispersion

An important characteristic of any set of data is the variation in the data. The degree to which the numerical data tends to vary about an average value is called the dispersion or scatteredness of the data.

The following are the measures of dispersion:

  • Range

  • Mean Deviation

  • Standard deviation

  • Variance

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Representation of Data

The data once collected must be arranged or organized in a way so that inferences or conclusions can be made out from it. The frequency of any value is the number of times that value appears in a data set.

The following are the ways to for the representation of data,

  1. Ungrouped distribution
  2. Ungrouped frequency distribution
  3. Grouped frequency distribution

Recommended Video Based on Statistics

Solved Examples on Statistics

Example 1: What is the range of the data $3,8,6,5,2,1,9,3,2$ ?
1) $9$
2) $10$
3) $8$
4) $5$

Solution:
Range :
The range is the difference between the smallest and largest observations. It is the simplest measure of Dispersion.

$
\text { Range }=9-1=8
$
Hence, the answer is an option 3.

Example 2: If the standard deviation of the numbers $2,3, a$ and $11$ is $3.5$, then which of the following is true?
1)$
3 a^2-26 a+55=0
$

2)$
3 a^2-32 a+84=0
$

3)$
3 a^2-34 a+91=0
$

4)$
3 a^2-23 a+44=0
$

Solution:
$
\begin{aligned}
& S D=\sqrt{\frac{\sum x_i^2}{n}-\left(\frac{\sum x_i}{n}\right)^2} \\
& \Rightarrow 3.5^2=\frac{49}{4}=\frac{4+9+a^2+121}{4}-\left(\frac{16+a}{4}\right)^2 \\
& \Rightarrow 3 a^2-32 a+84=0
\end{aligned}
$

Hence, the answer is the option 2.

Example 3: If the mean of the data : $7,8,9,7,8,7, \lambda, 8$ is $8$ , then the variance of this data is :
1) $\frac{7}{8}$
2) $1$
3) $\frac{9}{8}$
4) $2$

Solution:
$
\begin{aligned}
& \text { mean of data }=\frac{7+8+9+7+8+7+7+8}{8}=8 \\
& \Rightarrow \lambda=10
\end{aligned}
$

Variance
$
\begin{aligned}
& V^2=\frac{(7-8)^2+(8-8)^2+(9-8)^2+(7-8)^2+0^2+(7-8)^2+(10-8)^2+(8-8)^2}{8} \\
& =\frac{8}{8}=1
\end{aligned}
$

Variance $=1$
Hence, the answer is the option 2.

Example 4: The mean of 5 observations is $5$ and their variance is $124$. If three of the observations are $1,2$ and $6$; then the mean deviation from the mean of the data is :
1) $2.4$
2) $2.8$
3) $2.5$
4) $2.6$

Soluiton:
$
\begin{aligned}
& \frac{\sum x_i}{5}=5 \Rightarrow \sum x_i=25 \\
& \frac{\sum x_i^2}{n}-\left(\frac{\sum x_i}{n}\right)^2=124 \\
& \frac{\sum x_i^2}{5}-25=124 \\
& \sum x_i^2=149 \times 5=745
\end{aligned}
$

Let the two observations be $a$ and $b$
$a+b+1+2+6=25$
$a+b=16$
$a^2+b^2+1^2+2^2+6^2=745$
$a^2+b^2+1+4+36=745$
$a^2+b^2=704$
Mean deviation $=\frac{\sum\left|x_i-5\right|}{5}=\frac{\left|x_1-5\right|+\left|x_2-5\right|+8}{5}$

$
=\frac{8+\left|x_1-5\right|+\left|11-x_1\right|}{5}=\frac{8+6}{5}=2.8
$

Hence, the answer is option (2).

Example 5: All the students of a class performed poorly in Mathematics. The teacher decided to give grace marks of $10$ to each of the students. Which of the following statisticis correct?
1) variance
2) mean
3) median
4) mode

Solution:
Mean, Mode, and Median are the measures of central tendency. All of these change with change in any observation.

Variance is the measure of the scattering of data. It is a measure of dispersion which do not change if every given observation changes by the same amount.

The measures of central tendency will change, but not measures of dispersion.
So variance will not change.

Hence, the answer is the option (1).

Summary

Statistics are an important part of mathematics. These methods are widely used in real-life applications providing insights and solutions to complex problems. Mastery of these concepts can help in solving gaining deeper insights and contributing meaningfully to real-life problems.



Frequently Asked Questions (FAQs)

1. What do you mean by Statistics?

Statistics is a mathematical science including methods of collecting, organizing, and analyzing data so that meaningful conclusions can be drawn from them.

2. What are the types of data?

There are two types of data. Namely, Quantitative data and Qualitative data. 

3. What are the measures of dispersion?

The measures of dispersion are range, mean deviation, standard deviation and variance.

4. What are the methods of data collection?

The data collection methods are surveys, experiments and observational studies.

5. What are the measures of central tendency?

The measures of central tendency are mean, median and mode.

6. What is a population in statistics?
A population is the entire group of individuals, items, or events that you want to study or draw conclusions about. It's the complete set from which a sample is taken.
7. How does a sample differ from a population?
A sample is a subset of the population, selected to represent the larger group. While a population includes all members of the group being studied, a sample is a smaller, more manageable group used to make inferences about the population.
8. What is the difference between qualitative and quantitative data?
Qualitative data describes qualities or characteristics and is non-numerical (e.g., colors, opinions). Quantitative data is numerical and can be measured or counted (e.g., height, temperature, number of items).
9. What is a variable in statistics?
A variable is a characteristic or attribute that can be measured or observed and may have different values for different individuals or items in a study. Examples include height, age, or income.
10. What are the four levels of measurement in statistics?
The four levels of measurement are nominal (categories), ordinal (ordered categories), interval (equal intervals, no true zero), and ratio (equal intervals with a true zero point).
11. What is the difference between mean, median, and mode?
Mean is the average of all values, calculated by summing all values and dividing by the number of values. Median is the middle value when data is ordered. Mode is the most frequently occurring value. These are all measures of central tendency but can be affected differently by extreme values or skewed distributions.
12. What is the purpose of standardization in statistics?
Standardization transforms variables to a common scale, typically with a mean of 0 and a standard deviation of 1. This allows for easier comparison between variables with different units or scales and is often used in advanced statistical techniques.
13. What is ANOVA (Analysis of Variance)?
ANOVA is a statistical method used to compare means across three or more groups. It helps determine if there are any statistically significant differences between the means of independent groups.
14. What is the difference between a parameter and a statistic?
A parameter is a numerical characteristic of a population (e.g., population mean), while a statistic is a numerical characteristic of a sample (e.g., sample mean). Statistics are used to estimate population parameters.
15. What is the purpose of sampling in statistics?
Sampling is used to gather information about a population when it's impractical or impossible to study every member. It allows researchers to make inferences about the larger population based on a smaller, representative group.
16. What is standard deviation and why is it important?
Standard deviation is a measure of variability that indicates how spread out the values in a dataset are from the mean. It's important because it helps us understand the distribution of data and compare different datasets.
17. What is a confidence interval?
A confidence interval is a range of values that likely contains the true population parameter with a certain level of confidence. It provides a measure of the uncertainty associated with a sample estimate.
18. What is the law of large numbers?
The law of large numbers states that as the sample size increases, the sample mean tends to get closer to the population mean. This principle underlies many statistical methods and helps explain why larger samples generally provide more accurate estimates.
19. What is statistical significance?
Statistical significance indicates that an observed effect or relationship in a sample is unlikely to have occurred by chance alone. It's typically determined by comparing the p-value to a predetermined significance level (often 0.05).
20. What is regression analysis?
Regression analysis is a statistical method used to examine the relationship between one or more independent variables and a dependent variable. It helps predict the value of the dependent variable based on the independent variables.
21. What is a frequency distribution?
A frequency distribution is a table or graph that shows how often each value or category of a variable occurs in a dataset. It helps organize and summarize data for easier analysis and interpretation.
22. What is the normal distribution and why is it significant in statistics?
The normal distribution, also known as the bell curve, is a symmetrical, bell-shaped distribution where most data falls near the mean. It's significant because many natural phenomena follow this distribution, and it's the basis for many statistical tests and theories.
23. What is the Central Limit Theorem and why is it important?
The Central Limit Theorem states that the sampling distribution of the mean approaches a normal distribution as the sample size increases, regardless of the population distribution. This is important because it allows us to use normal distribution-based statistical methods even when the population distribution is unknown.
24. What is the difference between correlation and causation?
Correlation indicates a relationship or association between variables, while causation implies that one variable directly causes a change in another. Correlation does not necessarily imply causation, as there may be other factors influencing the relationship.
25. What is bias in sampling, and why is it important to avoid?
Bias in sampling occurs when certain members of a population are more or less likely to be included in the sample, leading to unrepresentative results. It's important to avoid bias to ensure that conclusions drawn from the sample accurately reflect the population.
26. What is statistics and why is it important?
Statistics is the science of collecting, analyzing, interpreting, and presenting data. It's important because it helps us make sense of large amounts of information, identify patterns, and make informed decisions in various fields like science, business, and social sciences.
27. What's the difference between descriptive and inferential statistics?
Descriptive statistics summarize and describe data using measures like mean, median, and standard deviation. Inferential statistics use sample data to make predictions or draw conclusions about a larger population.
28. What is a hypothesis in statistics?
A hypothesis is a testable statement or prediction about a population parameter based on sample data. It's used in inferential statistics to make decisions about populations using sample information.
29. What is the difference between null and alternative hypotheses?
The null hypothesis (H0) typically states that there is no effect or difference, while the alternative hypothesis (Ha) suggests that there is an effect or difference. Statistical tests are designed to either reject or fail to reject the null hypothesis.
30. What is a p-value and how is it interpreted?
A p-value is the probability of obtaining results at least as extreme as those observed, assuming the null hypothesis is true. A small p-value (typically < 0.05) suggests strong evidence against the null hypothesis, leading to its rejection.
31. What is the difference between discrete and continuous variables?
Discrete variables can only take on specific, countable values (e.g., number of children). Continuous variables can take on any value within a range and can be measured to increasing levels of precision (e.g., height, weight).
32. What is a Type I error in hypothesis testing?
A Type I error occurs when we reject the null hypothesis when it is actually true. It's also known as a "false positive." The probability of a Type I error is equal to the significance level (α) chosen for the test.
33. What is a Type II error in hypothesis testing?
A Type II error occurs when we fail to reject the null hypothesis when it is actually false. It's also known as a "false negative." The probability of a Type II error is denoted by β, and 1-β represents the power of the test.
34. What is the difference between a one-tailed and two-tailed test?
A one-tailed test examines the possibility of a relationship in one direction, while a two-tailed test considers the possibility of a relationship in both directions. Two-tailed tests are more conservative and are used when the direction of the effect is not predicted.
35. What is a contingency table?
A contingency table, also known as a cross-tabulation or crosstab, is a type of table that displays the frequency distribution of variables in a matrix format. It's used to study the relationship between two or more categorical variables.
36. What is the chi-square test used for?
The chi-square test is used to determine if there is a significant association between categorical variables. It compares observed frequencies to expected frequencies and is commonly used in analyzing contingency tables.
37. What is the difference between parametric and non-parametric tests?
Parametric tests assume that the data follows a specific distribution (usually normal) and work with parameters like mean and standard deviation. Non-parametric tests don't make assumptions about the underlying distribution and often work with ranks or orders of data.
38. What is a z-score and how is it interpreted?
A z-score represents how many standard deviations an observation is from the mean. It allows for comparison of values from different normal distributions. A z-score of 0 is at the mean, positive z-scores are above the mean, and negative z-scores are below the mean.
39. What is the purpose of data transformation in statistics?
Data transformation is used to change the scale or distribution of a variable. Common reasons include making the data more normally distributed, stabilizing variance, or linearizing relationships between variables. Examples include log transformation and square root transformation.
40. What is multicollinearity and why is it a problem in regression analysis?
Multicollinearity occurs when independent variables in a regression model are highly correlated with each other. It's a problem because it can lead to unstable and unreliable estimates of regression coefficients, making it difficult to determine the individual effects of predictors.
41. What is the difference between a population parameter and a sample statistic?
A population parameter is a numerical characteristic of an entire population, while a sample statistic is a numerical characteristic calculated from a sample. Sample statistics are used to estimate population parameters when studying the entire population is not feasible.
42. What is the purpose of bootstrapping in statistics?
Bootstrapping is a resampling technique used to estimate the sampling distribution of a statistic by repeatedly sampling with replacement from the original dataset. It's useful for making inferences when the underlying distribution is unknown or when sample sizes are small.
43. What is the difference between a probability and a likelihood in statistics?
Probability refers to the chance of an event occurring and is calculated based on a known model or distribution. Likelihood, on the other hand, is the probability of observing the data given a specific model or parameter values. Likelihood is often used in parameter estimation and hypothesis testing.
44. What is the purpose of data visualization in statistics?
Data visualization helps to present complex data in a graphical or pictorial format, making it easier to understand patterns, trends, and relationships within the data. It aids in exploratory data analysis, communicating results, and identifying potential outliers or anomalies.
45. What is the difference between a histogram and a bar chart?
A histogram displays the distribution of continuous data by dividing it into intervals (bins) and showing the frequency of data points in each interval. A bar chart, on the other hand, is used for categorical data, with each bar representing a distinct category.
46. What is the purpose of a box plot (box-and-whisker plot)?
A box plot provides a visual summary of the distribution of a dataset, showing the median, quartiles, and potential outliers. It's useful for comparing distributions across groups and identifying skewness or unusual data points.
47. What is the difference between a population standard deviation and a sample standard deviation?
The population standard deviation is calculated using all values in a population, while the sample standard deviation is calculated from a sample. The sample standard deviation uses n-1 in the denominator (Bessel's correction) to provide an unbiased estimate of the population standard deviation.
48. What is the purpose of stratified sampling?
Stratified sampling involves dividing the population into homogeneous subgroups (strata) and then selecting samples from each stratum. This ensures that important subgroups are represented in the sample and can improve the precision of estimates compared to simple random sampling.
49. What is the difference between independent and dependent variables in an experiment?
Independent variables are manipulated or controlled by the researcher and are expected to influence the outcome. Dependent variables are the outcomes or responses that are measured and are expected to change based on the independent variables.
50. What is the purpose of a scatterplot in statistics?
A scatterplot is used to visualize the relationship between two continuous variables. It helps identify patterns, trends, and the strength and direction of relationships between variables. Scatterplots are often used in correlation and regression analyses.
51. What is the difference between a population correlation coefficient and a sample correlation coefficient?
The population correlation coefficient (ρ) measures the strength and direction of the linear relationship between two variables in an entire population. The sample correlation coefficient (r) is an estimate of the population correlation based on a sample. The sample correlation is used to make inferences about the population correlation.
52. What is the purpose of a residual plot in regression analysis?
A residual plot shows the differences between observed values and predicted values (residuals) from a regression model. It helps assess the assumptions of the regression model, such as linearity, homoscedasticity, and the presence of outliers or influential points.
53. What is the difference between a simple random sample and a systematic sample?
In a simple random sample, each member of the population has an equal chance of being selected. In a systematic sample, every kth item is selected from a list after a random starting point. Systematic sampling can be more convenient but may introduce bias if there are patterns in the population list.
54. What is the purpose of a power analysis in statistical testing?
A power analysis helps determine the sample size needed to detect a specific effect size with a given level of confidence. It balances the risk of Type I and Type II errors and ensures that a study has a good chance of detecting a true effect if one exists.
55. What is the difference between a point estimate and an interval estimate?
A point estimate is a single value used to estimate a population parameter, such as the sample mean estimating the population mean. An interval estimate, like a confidence interval, provides a range of values likely to contain the true population parameter, giving a measure of the estimate's precision.

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