In short: Probability assignment help from FLN Tutors means a statistics tutor solves your counting, conditional probability, Bayes' theorem, distribution and hypothesis-testing questions with you step by step. Tutors explain counting, conditional probability and Bayes' theorem, random variables, the binomial, Poisson and normal distributions, expectation and variance, estimation, hypothesis tests and regression step by step, so you can solve similar problems yourself. Matched to a verified specialist who knows your course, your university's marking criteria, and your deadline.
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FLN offers one-to-one online Applied Probability and Statistics tutoring with verified subject specialists. Sessions focus on key models and theories, applied to real cases, including Probability rules, Conditional probability, Bayes' theorem and Random variables, using your own course material, so you understand the subject and can apply it yourself.
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Probability is the mathematics of uncertainty: it measures how likely events are. Statistics uses data to estimate unknown quantities and test claims. Applied probability and statistics brings them together to model real problems in engineering, science, business, computing and medicine.
A tutor uses the same method on your own questions, so you can repeat it in exams.
Most probability questions use a small set of rules. Knowing which one fits the wording is the main skill.
| Rule | Formula | Use it when the question says | ||
|---|---|---|---|---|
| Classical probability | P(A) = favourable outcomes ÷ total equally likely outcomes | A fair die, coin, card or random choice | ||
| Complement | P(not A) = 1 − P(A) | At least one, or not | ||
| Addition (or) | P(A ∪ B) = P(A) + P(B) − P(A ∩ B) | A or B; subtract the overlap unless the events are mutually exclusive | ||
| Multiplication (and) | P(A ∩ B) = P(A) × P(B | A) | A and B; use P(A) × P(B) only if independent | |
| Conditional probability | P(A | B) = P(A ∩ B) ÷ P(B) | Given that, or if we know that | |
| Bayes' theorem | P(A | B) = P(B | A) P(A) ÷ P(B) | Reversing a condition, such as a test result to a diagnosis |
| Permutations | nPr = n! ÷ (n − r)! | Arrangements where order matters | ||
| Combinations | nCr = n! ÷ [r!(n − r)!] | Selections where order does not matter | ||
| Expectation | E(X) = Σ x P(X = x) | Average or expected value | ||
| Variance | Var(X) = E(X²) − [E(X)]² | Spread of a random variable |
Two checks catch most errors: every probability must lie between 0 and 1, and the probabilities of all outcomes must add up to 1.
Probability and statistics help is one-to-one support from a tutor who explains each method step by step, works through problems from your own course and shows you how marks are awarded. At FLN Tutors it covers live online tutoring, worked homework solutions and guidance on probability and statistics assignments, so you understand the method and can use it yourself in the exam.
| What you need | Best option | What you get |
|---|---|---|
| Regular lessons through a probability or statistics course | Live one-to-one tutoring | A tutor explains the method on a shared whiteboard at your pace |
| A few questions you are stuck on | Homework help | Step-by-step worked solutions you can learn from |
| A graded probability assignment or statistics problem set | Assignment help | Tutor guidance, feedback and model workings within your university's rules |
This map lists the core probability and statistics topics, the point where students most often get stuck, and how a tutor helps you past it.
| Topic | Where students get stuck | How a tutor helps | ||
|---|---|---|---|---|
| Counting | Permutations versus combinations | Decide whether order matters before choosing a formula | ||
| Conditional probability | P(A | B) versus P(B | A) | Tree diagrams and two-way tables |
| Bayes' theorem | Setting up the prior and likelihoods | A table of joint probabilities for every case | ||
| Discrete distributions | Choosing binomial, Poisson or geometric | A checklist of each distribution's conditions | ||
| Continuous distributions | Standardising and reading tables | Sketch the curve and shade the area first | ||
| Expectation and variance | Rules for sums and linear transformations | Worked rules: E(aX + b) and Var(aX + b) | ||
| Hypothesis tests | Stating hypotheses and the conclusion | A five-step template ending in context | ||
| Regression | Interpreting coefficients and r² | Annotated output with plain-language meaning |
Choose a distribution by what you are counting or measuring and the conditions of the problem. Tutors teach you to check the conditions in writing, which is often worth marks.
| Distribution | Use when | Mean and variance |
|---|---|---|
| Binomial B(n, p) | A fixed number n of independent trials, each a success with probability p | np and np(1 − p) |
| Poisson Po(λ) | Counting random events in a fixed interval at a constant average rate λ | λ and λ |
| Geometric | Number of trials until the first success | 1/p and (1 − p)/p² |
| Normal N(μ, σ²) | Continuous, symmetric data, and many sums and averages | μ and σ² |
| Exponential | Waiting time between Poisson events | 1/λ and 1/λ² |
| Uniform | All values in a range equally likely | (a + b)/2 and (b − a)²/12 |
An illustrative question: 30% of components from a supplier need adjusting. In a random sample of 10, find P(exactly 3 need adjusting) and P(at most 2).
| Step | Working | Result |
|---|---|---|
| Model | X ~ B(10, 0.3): fixed n, independent items, constant p | — |
| P(X = 3) | C(10, 3) × 0.3³ × 0.7⁷ = 120 × 0.027 × 0.0824 | ≈ 0.2668 |
| P(X = 0), P(X = 1), P(X = 2) | 0.7¹⁰; 10 × 0.3 × 0.7⁹; 45 × 0.3² × 0.7⁸ | ≈ 0.0282, 0.1211, 0.2335 |
| P(X ≤ 2) | Add the three | ≈ 0.3828 |
| Mean and variance | np = 3; np(1 − p) = 2.1 | — |
A calculator's binomial cumulative function gives the same answers and is faster in exams.
An illustrative screening question: 1% of people have a condition. A test detects it 95% of the time, and gives a false positive for 5% of people without it. If a person tests positive, what is the probability they have the condition?
| Step | Working | |
|---|---|---|
| P(condition and positive) | 0.01 × 0.95 = 0.0095 | |
| P(no condition and positive) | 0.99 × 0.05 = 0.0495 | |
| P(positive) | 0.0095 + 0.0495 = 0.059 | |
| Bayes' theorem | P(condition | positive) = 0.0095 ÷ 0.059 ≈ 0.161 |
Only about 16% of positives have the condition, because the condition is rare. This is the classic base-rate effect.
An illustrative question: a help desk receives calls at an average rate of 2 per 10 minutes. Find the probability of exactly 3 calls in a 10-minute period and of at least one call.
| Step | Working | Result |
|---|---|---|
| Model | X ~ Po(2): events occur randomly and independently at a constant rate | — |
| P(X = 3) | e⁻² × 2³ ÷ 3! | ≈ 0.1804 |
| P(X ≥ 1) | 1 − P(X = 0) = 1 − e⁻² | ≈ 0.8647 |
An illustrative question: exam marks are normally distributed with mean 50 and standard deviation 8. Find the probability that a student scores under 62, over 40, and between 40 and 62.
| Step | Working | Result |
|---|---|---|
| Model | X ~ N(50, 8²) | — |
| Standardise 62 | z = (62 − 50) ÷ 8 | 1.5 |
| P(X < 62) | Φ(1.5) from tables or a calculator | ≈ 0.9332 |
| Standardise 40 | z = (40 − 50) ÷ 8 | −1.25 |
| P(X > 40) | 1 − Φ(−1.25) = Φ(1.25) | ≈ 0.8944 |
| P(40 < X < 62) | Φ(1.5) − Φ(−1.25) = 0.9332 − 0.1056 | ≈ 0.8276 |
Sketch the curve and shade the region before using tables: it shows at once whether you need Φ(z), 1 − Φ(z) or a difference.
An illustrative question: a committee of 3 is chosen at random from 5 men and 4 women. Find the probability that it contains exactly 2 women, and at least 1 woman.
| Step | Working | Result |
|---|---|---|
| Total committees | C(9, 3) = 84 | 84 |
| Exactly 2 women | C(4, 2) × C(5, 1) = 6 × 5 | 30 |
| P(exactly 2 women) | 30 ÷ 84 | ≈ 0.357 |
| No women | C(5, 3) = 10 | 10 |
| P(at least 1 woman) | 1 − 10 ÷ 84 = 74 ÷ 84 | ≈ 0.881 |
Order does not matter in a committee, so combinations are used. For 'at least one', the complement is almost always quicker than adding every case.
Most applied statistics courses move from probability to inference. Tutors teach a five-step method for every hypothesis test.
The same ideas appear at every level, but questions change in style and depth.
| Course or exam | Probability topics usually tested |
|---|---|
| CBSE and NCERT Class 10 | Classical probability with coins, dice and cards; complementary events |
| CBSE and NCERT Class 12 | Conditional probability, multiplication theorem, independent events, Bayes' theorem, random variables and the binomial distribution |
| GCSE and IGCSE | Tree diagrams, Venn diagrams, relative frequency, combined events |
| A-Level, IB and AP Statistics | Binomial and normal distributions, conditional probability, hypothesis testing |
| Aptitude and placement tests (CAT, GMAT, bank and campus exams) | Coins, dice, cards, balls in a bag and arrangements, solved quickly with combinations and complements |
| University and engineering courses | Random variables, joint distributions, expectation, limit theorems, stochastic processes |
School students can pair this with CBSE Class 12 Mathematics or A-Level Mathematics. For narrower university topics, see probability and statistical inference, statistics and probability for engineers and probability theory and random processes.
For free reference, see the NIST/SEMATECH e-Handbook of Statistical Methods and OpenStax's Introductory Statistics.
Tutors match your level, syllabus and exam board, from school to PhD.
| Level | What you study | Matching help |
|---|---|---|
| GCSE and IGCSE | Probability of events, tree diagrams, Venn diagrams, averages and charts | GCSE Edexcel Maths Higher, IGCSE Maths Extended |
| A-Level, IB and AP | Binomial and normal distributions, hypothesis testing, correlation; conditional probability | A-Level Mathematics, Master Statistics, IB Maths AI HL |
| Undergraduate | Probability theory, random variables, joint distributions, estimation, regression; probability for engineers | Probability Theory for Computer Engineers and homework help |
| Master's | Statistical inference, stochastic processes, Bayesian methods, applied modelling | Assignment help and live tutoring |
| PhD | Statistical modelling for research and help reporting results | Dissertation help |
Most lost marks come from the set-up rather than the arithmetic. Tutors check your work against a list like this one.
| Mark-loser | What to do instead | ||
|---|---|---|---|
| Not stating the distribution and its conditions | Write X ~ B(n, p) and justify independence and constant p | ||
| Confusing P(A | B) with P(B | A) | Draw a tree or two-way table |
| Adding probabilities of events that are not mutually exclusive | Use P(A ∪ B) = P(A) + P(B) − P(A ∩ B) | ||
| Wrong tail or inequality | Translate 'at least' and 'more than' carefully | ||
| Conclusions with no context | End with a sentence about the real situation | ||
| Rounding too early | Keep full values until the final answer |
Probability and statistics is assessed in several formats, and each one is marked on different things. Tutors help you understand the brief and the marking criteria before you start.
| Assessment | What markers look for |
|---|---|
| Problem sets and exams | Correct models, stated assumptions and clear working to a final answer |
| Statistics coursework | A clear question, appropriate data and methods, and interpreted results |
| Data-analysis projects | Correct software use, well-chosen tests and readable tables and graphs |
| Proof-based probability questions | Rigorous use of definitions and theorems |
| Dissertations | An analysis plan that fits the research question, reported clearly |
Probability is learned by hand; statistics usually adds software.
| Tool | What it is used for |
|---|---|
| Scientific or graphical calculator | Binomial, Poisson and normal probabilities in exams |
| R and RStudio | Simulation, distributions, tests and regression |
| Python (NumPy, SciPy, pandas) | Simulation and data analysis in engineering and computing courses |
| Excel | Descriptive statistics, charts and basic tests |
| SPSS or Minitab | Menu-driven tests in some courses |
Probability and statistics underpin engineering, computer science, economics, finance, actuarial science and the life sciences. Tutors link the method to your field's examples.
Related: Statistics, Biostatistics, Business Statistics, Data Science and Actuarial Science.
Probability and statistics is taught to similar core ideas worldwide, but grading, referencing and course structure differ. FLN tutors work from your own syllabus and handbook and schedule lessons in your time zone. Always follow your module handbook for referencing.
| Country | Where probability and statistics is studied | Grading | Referencing usually used |
|---|---|---|---|
| United Kingdom | GCSE and A-Level Maths, university statistics modules | GCSE 9–1, A-level A*–E; degrees First, 2:1, 2:2, Third | Few citations; Harvard or numbered if required |
| United States | AP Statistics, college probability and statistics courses | Letter grades A–F and a 4.0 GPA scale | Few citations; APA or numbered if required |
| Australia | Senior secondary maths and university statistics units | HD, D, C, P at university; ATAR for school leavers | Harvard if required |
| Canada | Data management courses and university statistics | Letter grades or percentages; GPA scales vary by province and university | APA or numbered if required |
| India | CBSE Classes 10 to 12, BSc Statistics, BTech probability papers; GATE | Board exam percentages; university CGPA, often on a 10-point scale | Few citations; as your university specifies |
Students in Ireland, the Gulf, Europe, Africa and Asia can book the same help, with lessons in their own time zone.
The price of probability and statistics help depends on the level, the number of questions and your deadline. A short A-Level binomial set costs less than a university stochastic processes assignment. You always see the price first: a fixed quote for homework and assignment guidance, or the tutor's hourly rate for live lessons, with no contract and no minimum number of sessions. Refunds follow our refund policy.
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A probability tutor works through the assignment with you rather than submitting it for you. They help you define the events, choose the right rule or distribution, set out the working and check your answers, so you understand each method and your final submission stays your own work.
You get step-by-step working for the methods your course uses, explanations of why each rule or distribution applies, practice questions similar to yours and a tutor check of your answers. Help can be live one-to-one lessons, written worked solutions or guidance on a graded assignment.
Short questions can often be answered the same day, depending on tutor availability. Longer problem sets, projects or university assignments depend on their size and your deadline, which you give when you ask for a quote. Asking early leaves time to go through the methods.
The price depends on the level, the number of questions and your deadline. A short school-level set costs less than a university stochastic processes assignment. You see the tutor's hourly rate or a fixed quote before you pay, with no contract and no minimum number of sessions.
Define the events or random variable in words, decide whether order matters and whether events are independent, then choose the rule or distribution. Write the formula, substitute the numbers and calculate. Finally, check the answer lies between 0 and 1 and makes sense in context.
Permutations count arrangements where order matters, such as codes or seating plans, using n! divided by (n − r)!. Combinations count selections where order does not matter, such as committees or hands of cards, using n! divided by r!(n − r)!.
Add the probabilities of A and B, then subtract the probability that both happen, so you do not count the overlap twice. If the events are mutually exclusive and cannot both happen, the overlap is zero and you simply add the two probabilities.
Divide the probability that both A and B happen by the probability of B. A two-way table or tree diagram makes this easier, because you can read the joint probability and the total for B directly. Do not confuse it with the probability of B given A.
Use a tree diagram for events that happen in stages, especially when later probabilities depend on earlier outcomes, such as drawing without replacement. Use a Venn diagram when events overlap at one point in time and you need unions, intersections or complements.
Use it when there is a fixed number of trials, each trial has only two outcomes, the probability of success is the same every time and the trials are independent. Writing these four conditions in your answer often earns method marks, even before any calculation.
Use the Poisson distribution to count random events in a fixed interval of time or space, when events happen independently and at a constant average rate. Its mean and variance are both equal to the rate, which is a quick check that the model fits the data.
Convert the value to a z-score by subtracting the mean and dividing by the standard deviation. Then read the probability from standard normal tables or a calculator. Sketching the curve and shading the region tells you whether you need the left tail, the right tail or a difference.
Bayes' theorem reverses a conditional probability. It finds the probability of a cause given an observed result, such as having a condition given a positive test. It combines the prior probability with how likely the result is under each possible cause.
For a discrete random variable, the expectation is the sum of each value multiplied by its probability. The variance is the expectation of X squared minus the square of the expectation. For standard distributions, use the formulas directly, such as np for a binomial mean.
Independent events do not affect each other's probabilities, so the probability of both is the product of their probabilities. Mutually exclusive events cannot happen together, so the probability of both is zero. Events with non-zero probabilities cannot be both independent and mutually exclusive.
Yes. The NCERT Class 12 Mathematics probability chapter covers conditional probability, the multiplication theorem, independent events, Bayes' theorem, random variables with their mean and variance, and the binomial distribution. Tutors help with board-style questions and with the reasoning behind each formula.
Learn the common question types, including coins, dice, cards, balls drawn from a bag and arrangements. Practise using combinations and the complement rule to save time, and check whether items are replaced. Timed practice with feedback on each mistake improves both speed and accuracy.
Probability starts from a known model and predicts how likely outcomes are. Statistics works the other way, using observed data to estimate unknown quantities and test claims about the model. Most applied courses teach probability first because statistical inference depends on it.
Yes, when it supports your learning. Tutoring, explanations, feedback and practice on similar problems are generally allowed. Submitting work done by someone else is not. Check your institution's academic integrity policy, and use the tutor's guidance to set out and solve your own problems.
Yes. Your files, questions and personal details are kept private and shared only with the tutor working on your request. We do not publish your work or contact your school or university, and you can ask for your files to be deleted once your request is complete.
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