Bayes Theorem is the extension of Conditional probability. Conditional probability helps us to determine the probability of A given B, denoted by P(A|B). So Bayes’ theorem says if we know P(A|B) then we can determine P(B|A), given that P(A) and P(B) are known to us.
Probability is used to predict the likelihood of the future event.
Statistics is used to analyse the past events
Probability tells us what will happen in a given ideal world?
While Statistics tells about how ideal is the world?
Probability is the basics of Inferential Statistics.
Variance and Standard Deviation are the most commonly used measures of variability and spread. Variability and spread are nothing but the process to know how much data is being varying from the mean point.
Types of Statistics: Descriptive vs Inferential
Basic terminology like Population vs Sample
Types of Variables: Numerical vs Categorical
Measures of central tendencies: Mean, Median and Mode and their specific use cases
Measures of dispersion/spread: Variance, standard deviation etc.