Welcome to our p-value calculator! friend will never ever again need to wonder how to discover the p-value, as here you deserve to determine the one-sided and also two-sided p-values from check statistics, following all the most famous distributions: normal, t-Student, chi-squared, and also Snedecor's F.

You are watching: The test statistic equals -1.50 what would be the p-value?

P-values appear all end science, yet many people find the concept a little intimidating. Don't issue - in this write-up we define not only what the p-value is, but also how to translate p-values correctly. Have you ever been curious around how to calculate p-value by hand? We provide you v all the necessary formulae together well!


What is p-value?

Formally, the p-value is the probability the the check statistic will create values at the very least as extreme as the worth it developed for your sample. The is vital to remember the this probability is calculation under the assumption that the null theory is true!

More intuitively, p-value answer the question:Assuming that i live in a human being where the null theory holds, just how probable is the that, for an additional sample, the test I'm performing will generate a worth at least as extreme as the one ns observed because that the sample I currently have?

It is the alternative hypothesis which determines what "extreme" in reality means, for this reason the p-value relies on the alternate hypothesis the you state: left-tailed, right-tailed, or two-tailed.In recipe below, S means a check statistic, x for the worth it produced for a given sample, and Pr(event | H0) is the probability of one event, calculate under the presumption that H0 is true:

Left-tailed test: p-value = Pr(S ≤ x | H0)

Right-tailed test: p-value = Pr(S ≥ x | H0)

Two-tailed test:

p-value = 2 * min H0)

(By mina,b we signify the smaller sized number out of a and also b.)

If the circulation of the test statistic under H0 is symmetric around 0, climate p-value = 2 * Pr(S ≥ |x| | H0)

or, equivalently, p-value = 2 * Pr(S ≤ -|x| | H0)

As a photo is worth a thousands words, let us highlight these definitions. Right here we use the reality that the probability have the right to be neatly depicted as the area under the density curve for a given distribution. We offer two set of pictures: one because that a symmetric distribution, and the other for a it was crooked (non-symmetric) distribution.

Symmetric case: typical distribution

*

Non-symmetric case: chi-squared distribution

*

In the last picture (two-tailed p-value for it was crooked distribution), the area that the left-hand side is equal to the area the the right-hand side.


How to calculate p-value from test statistic?

To determine the p-value, you must know the distribution of her test statistic under the assumption that the null hypothesis is true. Then, with aid of the accumulation distribution role (cdf) the this distribution, we have the right to express the probability that the test statistics being values at least as excessive as its value x because that the sample:

Left-tailed test: p-value = cdf(x)

Right-tailed test: p-value = 1 - cdf(x)

Two-tailed test: p-value = 2 * mincdf(x) , 1 - cdf(x)

If the distribution of the check statistic under H0 is symmetric around 0, climate a two-sided p-value can be simplified as p-value = 2 * cdf(-|x|), or, equivaently, asp-value = 2 - 2 * cdf(|x|)

The probability distributions the are most widespread in hypothesis trial and error tend come have complex cdf formulae, and finding the p-value by hand might not be possible. You'll most likely need to resort to a computer, or come a statistical table, where world have gathered almost right cdf values.

Well, you currently know just how to calculate p-value, but... Why carry out you need to calculate this number in the very first place? In hypothesis testing, the p-value technique is an alternate to the crucial value approach. Recall the the last requires researchers to pre-set the meaning level, α, i beg your pardon is the probability of rejecting the null hypothesis when it is true (so the type ns error). When you have your p-value, you simply need to compare it with any kind of given α to conveniently decide even if it is or not to refuse the null theory at that significance level, α. Because that details, examine the next section, wherein we describe how to translate p-values.


How to analyze p-value?

As we have mentioned above, p-value is the answer to the complying with question:

Assuming that i live in a civilization where the null theory holds, how probable is the that, for another sample, the check I'm performing will certainly generate a worth at the very least as excessive as the one ns observed for the sample I currently have?

What go that average for you? Well, you've acquired two options:

A high p-value way that her data is extremely compatible with the null hypothesis; andA small p-value gives evidence versus the null hypothesis, together it method that your an outcome would be very improbable if the null hypothesis were true.

However, the may occur that the null theory is true, but your sample is very unusual! for example, imagine we studied the impact of a brand-new drug, and also get a p-value that 0.03. This way that, in 3% of comparable studies, random possibility alone would certainly still have the ability to produce the worth of the test statistic that us obtained, or a worth even much more extreme, also if the drug had actually no impact at all!

The question "what is p-value" can likewise be answered as follows: p-value is the smallest level of significance at i m sorry the null hypothesis would certainly be rejected. So, if you currently want come make a decision around the null hypothesis in ~ some significance level α, simply compare her p-value v α:

If p-value ≤ α, then you reject the null hypothesis and accept the alternate hypothesis; andIf p-value ≥ α, then you don't have sufficient evidence come reject the null hypothesis.

Obviously, the fate of the null hypothesis counts on α. Because that instance, if the p-value was 0.03, we would reject the null hypothesis at a meaning level that 0.05, yet not in ~ a level that 0.01. That's why the meaning level must be declared in advance, and also not adjusted conveniently after p-value has actually been established! A significance level of 0.05 is the most usual value, but there's nothing magical about it. Here, you deserve to see what too solid a belief in the 0.05 threshold have the right to lead to. It's constantly best come report the p-value, and permit the leader to make their very own conclusions.

Also, be affected by each other in mind the subject area expertise (and typical reason) is crucial. Otherwise, mindlessly using statistical principles, friend can quickly arrive in ~ statistically significant, in spite of the conclusion gift 100% untrue.


How to usage the p-value calculator to discover p-value from test statistic?

As our p-value calculator is below at your service, you no much longer need come wonder how to uncover p-value from every those complex test statistics! right here are the actions you should follow:

Pick the alternative hypothesis: two-tailed, right-tailed, or left-tailed.

Tell united state the distribution of her test statistic under the null hypothesis: is that N(0,1), t-Student, chi-squared, or Snedecor's F? If you space unsure, examine the sections below, as they are dedicated to these distributions,.

If needed, point out the degrees of freedom of the test statistic's distribution.

Enter the value of test statistic computed for her data sample.

Our calculator determines the p-value from check statistic, and provides the decision to it is in made about the null hypothesis. The standard definition level is 0.05 by default.

Go to the advanced mode if you must increase the precision through which the calculations are performed, or change the significance level.


How to discover p-value indigenous z-score?

In regards to the accumulation distribution role (cdf) of the typical normal distribution, i beg your pardon is traditionally denoted through Φ, p-value is offered by the following formulae:

Left-tailed z-test:p-value = Φ(Zscore)

Right-tailed z-test:p-value = 1 - Φ(Zscore)

Two-tailed z-test:p-value = 2 * Φ(−|Zscore|) or p-value = 2 - 2 * Φ(|Zscore|)

We usage the Z-score if the test statistic around follows the standard normal distribution N(0,1). Many thanks to the central limit theorem, you have the right to count on the approximation if you have actually a huge sample (say at the very least 50 data points), and treat your circulation as normal.

A Z-test most regularly refers come testing the population mean, or the difference between two populace means, in particular between 2 proportions. Girlfriend can likewise find Z-tests in preferably likelihood estimations.

*
density of the traditional normal distribution StefanPohl / CC0 wikimedia.org

How to find p-value from t?

The p-value indigenous t-score is offered by the complying with formulae, in which cdft,d represents the accumulation distribution role of the t-Student circulation with d levels of freedom:

Left-tailed t-test:p-value = cdft,d(tscore)

Right-tailed t-test:p-value = 1 - cdft,d(tscore)

Two-tailed t-test:p-value = 2 * cdft,d(−|tscore|)

or p-value = 2 - 2 * cdft,d(|tscore|)

Use the t-score alternative if your test statistic follows the t-Student distribution. This distribution has a shape similar come N(0,1) (bell-shaped and also symmetric), yet has heavier tails - the exact shape counts on the parameter referred to as the degrees the freedom. If the number of degrees of freedom is large (>30), i m sorry generically wake up for large samples, the t-Student circulation is virtually indistinguishable from normal circulation N(0,1).

*
thickness of the t-distribution through ν degrees of freedom Skbkekas / CC BYwikimedia.org

The most usual t-tests space those because that population means through an unknown populace standard deviation, or because that the difference between way of two populations, v either equal or unequal however unknown population standard deviations. There's also a t-test for paired (dependent) samples.


p-value native chi-square score (χ2 score)

Use the χ²-score option once performing a test in which the check statistic complies with the χ²-distribution.This distribution arises, if, because that example, you take it the sum of squared variables, each following the normal distribution N(0,1). Psychic to check the variety of degrees of liberty of the χ²-distribution of your test statistic!

*
density of the χ²-distribution v k levels of liberty Geek3 / CC BYwikimedia.org

How to uncover the p-value native chi-square-score? You deserve to do that with assist of the adhering to formulae, in which cdfχ²,d denotes the accumulation distribution role of the χ²-distribution through d degrees of freedom:

Left-tailed χ²-test:p-value = cdfχ²,d(χ²score)

Right-tailed χ²-test:p-value = 1 - cdfχ²,d(χ²score)

Remember the χ²-tests because that goodness-of-fit and independence space right-tailed tests! (see below)

Two-tailed χ²-test: p-value =

2 * mincdfχ²,d(χ²score), 1 - cdfχ²,d(χ²score)

(By mina,b we denote the smaller sized of the number a and also b.)

The most famous tests which lead to a χ²-score space the following:

Goodness-of-fit test checks whether the empirical (sample) circulation agrees with some intended probability distribution. In this case, the test statistic follows the χ²-distribution with k - 1 degrees of freedom, where k is the variety of classes into which the sample is divided. This is a right-tailed test.

Independence test is offered to determine if there is a statistically far-ranging relationship between two variables. In this case, its check statistic is based upon the contingency table and also follows the χ²-distribution with (r - 1)(c - 1) levels of freedom, whereby r is the number of rows and also c the number of columns in this contingency table. This also is a right-tailed test.


p-value native F-score

Finally, the F-score option need to be provided when you execute a check in i beg your pardon the check statistic adheres to the F-distribution, also known together the Fisher–Snedecor distribution. The precise shape of one F-distribution counts on two levels of freedom.

*
thickness of the F-distribution through (d1,d2)-degrees of flexibility IkamusumeFan / CC BY-SA wikimedia.org

To see where those levels of freedom come from, consider the independent random variables X and also Y, which both follow the χ²-distributions v d1 and also d2 levels of freedom, respectively. In that case, the ratio (X/d1)/(Y/d2) complies with the F-distribution, v (d1, d2)-degrees the freedom. Because that this reason, the two parameters d1 and d2 are also called the numerator and also denominator levels of freedom.

The p-value native F-score is given by the adhering to formulae, where we allow cdfF,d1,d2 signify the accumulation distribution role of the F-distribution, v (d1, d2)-degrees the freedom:

Left-tailed F-test:p-value = cdfF,d1,d2(Fscore)

Right-tailed F-test:p-value = 1 - cdfF,d1,d2(Fscore)

Two-tailed F-test: p-value =

2 * mincdfF,d1,d2(Fscore), 1 - cdfF,d1,d2(Fscore)

(By mina,b we denote the smaller of the number a and b.)

Below us list the most necessary tests that produce F-scores. All the them space right-tailed tests.

A test because that the equality of variances in 2 normally dispersed populations. Its check statistic adheres to the F-distribution with (n - 1, m - 1)-degrees the freedom, wherein n and also m are the particular sample sizes.

ANOVA is supplied to test the equality of way in 3 or more groups that come indigenous normally spread populations through equal variances. Us arrive at the F-distribution through (k - 1, n - k)-degrees that freedom, where k is the variety of groups, and also n is the full sample dimension (in all teams together).

A check to compare two nested regression models. The test statistic adheres to the F-distribution v (k2 - k1, n - k2)-degrees that freedom, wherein k1 and k2 are the variety of variables in the smaller and bigger models, respectively, and n is the sample size.

You may notice that the F-test of an as whole significance is a particular type of the F-test for comparing 2 nested models: it tests whether our design does significantly far better than the model with no predictors (i.e., the intercept-only model).


Can p-value it is in negative?

No, the p-value can not be negative, because the p-value is the probability the the test statistic satisfying certain conditions, as, together we every know, probabilities can not be negative.


What does a high p-value mean?

A high p-value way that under the null theory there's a high probability that for one more sample the test statistic will certainly generate a value at the very least as excessive as the one together observed for the sample you currently have. A high p-value doesn't permit you to reject the null hypothesis.

See more: How Many Inches Is 230 Mm To In 230 Millimeters To Inches, 230Mm To Inches


What go a short p-value mean?

A low p-value way that under the null theory there's tiny probability that for another sample the check statistic will certainly generate a value at least as extreme as the one together observed because that the sample you currently have. A low p-value is an evidence in donate of the different hypothesis - it permits you to reject the null hypothesis.