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Different Important Symbols In Statistics One Should Know

Different Important Symbols In Statistics

Are you a student of statistics? Then you are aware that statistics contain different formulas. As statistics is a type of mathematical analysis. It also holds numerous formulas, just like mathematics. And those formulas contain several symbols as well. It is important for students to know and understand those symbols in statistics.

Yes, statistical symbols are vital to understand and learn. This will help you understand the formulas better. Moreover, it will also help you to learn which formula you can use for a specific purpose. 

So, in this blog, you will learn different important symbols in statistics and Statistics Assignment Help. However, keep scrolling to learn about the various symbols. Here we go!

Some Important Rational Symbols In Statistics


Symbols Description
= Same as / equals
≠  is not the same as/not equal.
> Greater than/ more than/ is above/ exceeds

or >=

is greater than or also equal to/is not less than.
< Is less than/is below

or <=

Is less than or equals to/is not greater than/is no more than/ 
A < x < B X is between A and B, exclusive
A ≤ x ≤ B X is between A and B, inclusive
A ≈ B A is approximately equal to B.

 The following are the symbols for various sample statistics and corresponding population parameters.


Sample Statistics Population Statistics Description
n N number of individuals in the population or sample.
x̅ “x-bar” μ “mu”

or μx

M or Med

or x̃ “x-tilde”

(None) Median

(TIs say Sx)

σ “sigma”

or σx

Standard Deviation

For variance, apply a squared symbol (s² or σ²).

r ρ “rho” Coefficient of linear correlation
“p-hat” p Proportion
z   t   χ² (n/a) Calculated Test Statistics

μ and σ both can show what the mean or standard deviation is being calculated for. For example, σx̅ (“sigma sub x-bar”) is the standard deviation of sample mean, or standard error of the mean.

More Important Letters/Symbols In Statistics

BD or BPD (Binomial Probability Distribution)

The binomial Model also binomial probability distribution or BPD, is a type of discrete probability distribution that refers to Bernoulli tests. Especially when there are a fixed number of trials, n.

CI (Confidence Interval)

A confidence interval estimate is a statement of limitations for a population parameter. Moreover, it includes your degree of confidence that the parameter really falls in those limitations.

CLT (Central Limit Theorem)

According to the theorem’s equivalent, if you take a number of independent random variables and total up their values. Then the variable will be more independent. And the result will be closer to a normal distribution (ND).

d: It is the difference between paired data.

df or v “nu” : it is a degree of freedom in a student’s t or x^2 distribution.

DPD (Discrete Probability Distribution)

A discrete probability distribution provides the probabilities for each of the discrete random variable’s potential values. A table, a histogram, or a formula all can help to show the distribution. You can verify the probabilities in a DPD theoretically or empirically.

E: error of margin, also maximum estimate error.

f: Frequency

f/n: Relative Frequency

HT: Hypothesis Test

Ho (Null Hypothesis)

It is a statement that nothing is happening, there is no impact, and there is “nothing to see here”. Moreover, it is an equation that states that p (the population percentage) equals a specific number.

H1 or Ha (Alternative Hypothesis)

It is a statement that something is happening and there is an effect. Also, it is unequal. Because it suggests that p is not the same as the number given in H0.

IQR (Interquartile range)

The interquartile range is the difference between the first and third quartiles. It is a statistic that helps in characterizing variability in data sets with outliers.

m: Slope of the line.

ND: Normal Distribution

Another name for normal distribution is Gaussian distribution. However, it shows that the data that are close to the mean occur more regularly than data that are far from the mean. Moreover, the bell curve will represent a normal distribution on a graph.

p: Probability value

P(A): The probability of event A.

P(A^C) or P(not A): The probability in which A does not happen.

P [B | A]: This is the probability that event B will occur given, and also event A will undoubtedly occur.

P80: 80th Percentile

q: Probability of failure on each trial in geometric or binomial distribution. Here p s the probability of success in a single trial.

r: coefficient of linear correlation of a simple.

R^2: Determination Coefficient.

s: Sample’s Standard Deviation

SEM: Standard error of the mean.

SEP: Standard error of the proportion.

X: A variable.

z: standard score or a z-score.

z (area): The z-score is the area under the normal curve. It is located to the right of the normal curve. Moreover, it is not multiplication.

Greek Symbols in Statistics

α “alpha”: It is the acceptable probability of a Type I error. Also, it is a significant level in a hypothesis test.

1−α = confidence level

μ mu: It is a mean of a population.

ρ rho: Coefficient of the linear correlation of a population.

σ “sigma”: Population’s standard deviation

∑ “sigma”: Summation

χ² “chi-squared”: It is a distribution for contingency tables and multinomial experiments.

Final Words

To sum up, we have discussed the various symbols in statistics. These symbols will help you understand several formulas as well as concepts. So, if you want to master the field of statistics, the above symbols are vital for you. Learn and understand these symbols and try to remember them. These symbols will help you in any calculations. However, in the end, I hope this blog will be useful for you and that you understand all the above-mentioned symbols.

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