SAT Statistic Probability

SAT Statistics and Probability

Part 1: Introduction to Statistics and Probability

Welcome to our SAT prep course at MKSprep, Kathmandu, Nepal. This series is dedicated to helping you navigate the intricacies of Statistics and Probability, an essential part of the SAT mathematics section.

What is Statistics?

Statistics is the study of collecting, analyzing, interpreting, presenting, and organizing data. It involves techniques that can help us quantitatively understand the world around us.

What is Probability?

On the other hand, probability is the branch of mathematics that measures the likelihood that a given event will occur. This concept is used in a variety of everyday contexts, making it a valuable component of your mathematical toolkit.

In the SATs, these topics often intertwine as you analyze statistical data and use probability to make predictions about it.

Importance in SATs

Understanding Statistics and Probability is essential for the SATs because:

  1. They form a significant part of the math section.
  2. Real-world problems often require knowledge of these topics.
  3. The ability to interpret and analyze statistical data is a key skill that colleges value.

In this series, we will delve deeper into each of these topics and cover the following:

  1. Understanding Data: Types of data, data collection methods, and visual representation of data.
  2. Measures of Central Tendency: Mean, median, mode, and their implications.
  3. Probability Basics: Probability rules, events, and outcomes.
  4. Conditional Probability and Independence: Understanding dependent and independent events.
  5. Permutations and Combinations: The counting principles.
  6. Descriptive vs. Inferential Statistics: Understanding the difference and their uses.
  7. Common Mistakes and Tips: To avoid these mistakes and best practices for mastering statistics and probability.

Stay tuned for the next post, where we delve into understanding data.

[Next: Understanding Data]

Part 2: Understanding Data

Continuing our SAT prep course at MKSprep, Kathmandu, Nepal, let’s delve into the concept of data in the context of Statistics and Probability. Understanding data is the first step towards mastering these topics and acing the SATs.

Types of Data

In statistics, data is broadly classified into two types:

  1. Qualitative or Categorical Data: This type of data describes characteristics or qualities and is often represented in categories. Examples include types of movies (romance, action, drama), hair color, or nationality.
  2. Quantitative or Numerical Data: This type of data is numerical in nature and can be measured or counted. It’s further categorized into discrete data (which can only take specific values, like the number of students in a class) and continuous data (which can take any value within a range, like height or weight).

Data Collection Methods

Data can be collected using various methods:

  1. Observation: Data is collected by observing and recording phenomena as they occur.
  2. Surveys/Questionnaires: Data is collected by asking people questions and recording their answers.
  3. Experiments: Data is collected by conducting an experiment under controlled conditions.
  4. Existing Sources: Data is collected from already available resources, such as books, databases, or the internet.

Visual Representation of Data

Data can be visually represented using various methods:

  1. Bar Charts: Used for comparing the size of different categories.
  2. Pie Charts: Used for showing how the whole is divided into different parts.
  3. Histograms: Used for showing the distribution of numerical data.
  4. Line Graphs: Used for showing trends over time.
  5. Scatter Plots: Used for showing the relationship between two numerical variables.

Understanding how data is categorized, collected, and visually represented will help you better analyze and interpret the data, leading to more accurate solutions in the SAT math section.

In the next part, we’ll discuss the measures of central tendency: mean, median, and mode.

[Next: Measures of Central Tendency]

Part 3: Measures of Central Tendency

As we progress in our SAT prep series at MKSprep, Kathmandu, Nepal, we move to another critical topic in Statistics and Probability – Measures of Central Tendency. These are ways to describe the center of a data set.

There are three main measures of central tendency:

  1. Mean: The mean is what most people think of as the average. To calculate it, you add up all the numbers in your data set and then divide by the number of numbers. For instance, the mean of 2, 3, 7 is (2+3+7)/3 = 4.
  2. Median: The median is the middle value in a data set. To find it, you arrange your numbers in ascending order and find the number that falls exactly in the middle. If you have an even number of data points, you take the mean of the middle two. For example, the median of 2, 3, 7 is 3.
  3. Mode: The mode is the number that appears most frequently in your data set. A data set may have one mode, more than one mode, or no mode at all. For instance, in the data sets 2, 3, 3, and 7, the mode is 3.

Each of these measures can give you a different sense of the “middle” of your data. Here are some key points to remember:

  • The mean is influenced by every value in the data set, including extreme values.
  • Extreme values don’t influence the median. When you have a skewed distribution or outliers, the median is often the best measure of central tendency.
  • The mode can be used with any level of measurement (nominal, ordinal, interval, or ratio), and it is the only measure of central tendency that can be used with nominal data.

In our next session, we will explore the basic concepts and rules of probability.

[Next: Probability Basics]

Part 4: Probability Basics

Continuing our SAT preparation course at MKSprep, Kathmandu, Nepal, this post will introduce you to the basics of probability, a cornerstone of Statistics that finds considerable importance in the SAT mathematics section.

What is Probability?

Probability is the mathematical term for the likelihood that something will occur, such as drawing a certain card from a deck. It is expressed as a number between 0 and 1, where 0 implies that the event will not occur, and 1 implies that it will.

Basic Probability Principles

  1. Experiment: An experiment is a situation involving a chance or probability that leads to results called outcomes.
  2. Outcome: An outcome is the result of a single trial of an experiment.
  3. Event: An event is one or more outcomes of an experiment.
  4. Probability of an Event: The probability of an event is given by the ratio of the number of favorable outcomes to the total number of outcomes. It’s often expressed as P(A) = Number of favorable outcomes / Total number of outcomes.

Types of Events

  1. Independent Events: Two events are independent if one event’s outcome does not affect another’s outcome. For example, flipping a coin twice.
  2. Dependent Events: Two events are dependent if the outcome of the first event affects the outcome of the second event. For example, drawing two cards from a deck without replacement.
  3. Mutually Exclusive Events: Two events are mutually exclusive if they cannot happen at the same time. For example, a single coin flip cannot result in both a head and a tail.

Understanding probability basics will allow you to predict the likelihood of an event happening, which is a key concept in both statistics and probability. Stay tuned for our next topic, where we dive deeper into conditional probability and independence.

[Next: Conditional Probability and Independence]

Part 5: Conditional Probability and Independence

Moving forward in our SAT preparation journey at MKSprep, Kathmandu, Nepal, this segment focuses on conditional probability and independence – two significant concepts in Statistics and Probability.

What is Conditional Probability?

Conditional probability is the probability of an event given that another event has occurred. Given two events A and B, the conditional probability of A given B is usually written as P(A|B).

If the event of interest is A and event B has already occurred, the conditional probability of A given B is defined as:

P(A|B) = P(A ∩ B) / P(B), if P(B) ≠ 0

What Does Independence Mean?

In probability theory, two events are independent if the occurrence of one does not affect the probability of the occurrence of the other. Equivalently, two events are independent if the probability of their intersection equals the product of their probabilities:

P(A ∩ B) = P(A) * P(B)

This also means that if A and B are independent events, then:

P(A|B) = P(A)

P(B|A) = P(B)

Real-World Applications

Understanding conditional probability and independence is key to interpreting data and making decisions under uncertainty. This understanding can guide us in areas as diverse as medical testing, insurance, and even understanding the weather forecast.

In the next part, we will take you through the concept of ‘probability distribution’ and ‘expected value,’ rounding up our discussion on probability.

[Next: Probability Distributions and Expected Value]

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Part 6: Probability Distributions and Expected Value

Continuing our SAT preparation journey at MKSprep, Kathmandu, Nepal, this section introduces you to probability distributions and the concept of expected value – essential concepts in the realm of Statistics and Probability.

Probability Distribution

A probability distribution is a statistical function describing all the possible values and likelihoods a random variable can take within a given range. The probability distribution for a random variable X defines the range for X and assigns probabilities to all possible values of X.

Types of Probability Distributions

There are several types of probability distributions, including:

  • Uniform Distribution: Here, all outcomes are equally likely; a common example is a roll of a fair die.
  • Normal Distribution: In a normal (or Gaussian) distribution, data is symmetrically distributed with no skew. Most values remain around the mean value, making the arrangement bell-shaped.

Expected Value

A random variable’s expected value (or mean) is the long-run average value of repetitions of the experiment it represents. For a single discrete random variable X, the expected value is given by:

E[X] = ∑ [x * P(X = x)]

where the sum is over all possible values of X.

Why Are These Concepts Important?

Understanding probability distributions and the expected value is essential in many areas, including insurance, business, economics, and computer science. It allows us to make predictions about a data set and understand the underlying patterns of random phenomena.

In our next post, we will dive into the topics of variance and standard deviation and how these concepts can be used to understand your data further.

[Next: Variance and Standard Deviation]

Part 7: Variance and Standard Deviation

Welcome back to our SAT preparation series at MKSprep, located in Kathmandu, Nepal. Today, we will cover the concepts of variance and standard deviation, both crucial to understanding the dispersion of data in Statistics and Probability.


Variance measures how far a set of numbers is spread out from their average value. It’s an expectation of the squared deviation of a random variable from its mean. In simple terms, it is the average of the squared differences from the mean.

For a population variance (denoted as σ²), it’s calculated as:

σ² = Σ[(xi – μ)²] / N


  • xi: each value from the dataset
  • μ: the mean of the dataset
  • N: the total number of data points

Standard Deviation

Standard deviation (represented by the symbol σ) is the variance’s square root. It is a more interpretable quantity than the variance as it is measured in the same units as the random variable, while variance is measured in squared units.

σ = √σ²

Significance of Variance and Standard Deviation

Both variance and standard deviation provide valuable insights about the dispersion of data values. They indicate how much, on average, each data point differs from the mean.

While the variance gives a rough idea of spread, the standard deviation is more concrete, giving you exact distances from the mean.

In the final part of our series, we will introduce the concept of ‘correlation’ and how it’s used to understand the relationship between variables.

[Next: Correlation and Regression]

Part 8: Correlation and Regression

Concluding our SAT preparation series on Statistics and Probability at MKSprep, Kathmandu, Nepal, let’s delve into the topics of correlation and regression – two fundamental concepts used for understanding the relationship between two or more variables.


Correlation is a statistical technique used to determine the degree to which two variables are related. It gives us the measure of the strength of a linear relationship between two variables.

A correlation coefficient, represented by ‘r,’ quantifies this relationship and ranges from -1 to +1:

  • +1 indicates a strong positive relationship.
  • -1 indicates a strong negative relationship.
  • A result of zero indicates no relationship at all.


Regression analysis is a powerful statistical method that allows us to examine the relationship between two or more variables of interest. While correlation can help identify the relationship’s strength, regression allows us to predict future outcomes.

The most common method of regression is the least squares method, which finds the best-fitting line through the observed data by minimizing the sum of the squares of the vertical deviations from each data point to the line.

Why Are These Concepts Important?

Understanding correlation and regression can help us to predict one variable from the knowledge of one or more other variables. It’s a commonly used tool in business for forecasting and modeling.

We hope this series has provided a strong foundation in Statistics and Probability for your SAT preparation. Remember, practice is key to mastering these concepts. Best of luck with your SAT preparation!

[End of series]