## How can I test that a value is "greater than or equal to" in Jasmine?

I want to confirm that a value is a decimal (or 0), so the number should be greater than or equal to zero *and* less than 1.

describe('percent',function(){ it('should be a decimal', function() { var percent = insights.percent; expect(percent).toBeGreaterThan(0); expect(percent).toBeLessThan(1); }); });

How do I mimic " >= 0 "?

**Assessment :: Values Assessment,** You can compare a sample mean to a hypothesized or target value using a one-sample t-test. You can compare the means of two groups with a two-sample t-test. If you have two groups with paired observations (e.g., before and after measurements), use the paired t-test. How do t-tests work? Take One of the Free Personality Tests DISC Assessment Myers-Briggs Test Enneagram Test Self-Esteem: The Rosenberg Test Career Test: RIASEC Holland Code Productivity Test

You just need to run the comparison operation first, and then check if it's truthy.

describe('percent',function(){ it('should be a decimal',function(){ var percent = insights.percent; expect(percent >= 0).toBeTruthy(); expect(percent).toBeLessThan(1); }); });

**What is a test statistic? - Minitab Express,** Learn how to compare a P-value to a significance level to make a conclusion in a significance Duration: 7:58
Posted: Feb 24, 2018 Divide the variance of each set of data by the number of values. Add the two resulting numbers together. Calculate the square root of the number you found in the above step. Take the number you got when you subtracted the two means and divide it by the square root you found in the above step. This is your T value.

The current version of Jasmine supports toBeGreaterThan and toBeLessThan.

expect(myVariable).toBeGreaterThan(0);

**Manually calculate a p-value - Minitab,** after finding the t value to be equal to -2.5, how do we find the p-value (i.e. probability of Duration: 5:02
Posted: Feb 24, 2018 Re: How do I - Logical Test: If a cell's value is not a numb Try =IF(ISERROR(your formula),"") Nimo

Somewhat strangley this isn't basic functionality

You can add a custom matcher like this:

JasmineExtensions.js

yourGlobal.addExtraMatchers = function () { var addMatcher = function (name, func) { func.name = name; jasmine.matchers[name] = func; }; addMatcher("toBeGreaterThanOrEqualTo", function () { return { compare: function (actual, expected) { return { pass: actual >= expected }; } }; } ); };

In effect you're defining a constructor for your matcher - it's a function that returns a matcher object.

Include that before you 'boot'. The basic matchers are loaded at boot time.

Your html file should look like this:

<!-- jasmine test framework--> <script type="text/javascript" src="lib/jasmine-2.0.0/jasmine.js"></script> <script type="text/javascript" src="lib/jasmine-2.0.0/jasmine-html.js"></script> <!-- custom matchers --> <script type="text/javascript" src="Tests/JasmineExtensions.js"></script> <!-- initialisation--> <script type="text/javascript" src="lib/jasmine-2.0.0/boot.js"></script>

Then in your boot.js add the call to add the matchers after jasmine has been defined but before jasmine.getEnv(). Get env is actually a (slightly misleadingly named) setup call.

The matchers get setup in the call to setupCoreMatchers in the Env constructor.

/** * ## Require & Instantiate * * Require Jasmine's core files. Specifically, this requires and attaches all of Jasmine's code to the `jasmine` reference. */ window.jasmine = jasmineRequire.core(jasmineRequire); yourGlobal.addExtraMatchers(); /** * Since this is being run in a browser and the results should populate to an HTML page, require the HTML-specific Jasmine code, injecting the same reference. */ jasmineRequire.html(jasmine); /** * Create the Jasmine environment. This is used to run all specs in a project. */ var env = jasmine.getEnv();

They show another way of adding custom matchers in the sample tests, however the way it works is to recreate the matcher(s) before every single test using a `beforeEach`

. That seems pretty horrible so I thought I'd go with this approach instead.

**Understanding t-Tests: t-values and t-distributions,** Similarly, if Ha is the greater-than alternative, you reject H0 only if the test statistic falls in the right tail (above 2). To find the p-value for your test statistic: Look up Conduct 2 sample t-tests for equal means to compare average results. Apply non-parametric two sample tests to compare the 2 distributions (ex. Kolmogorov-Smirnov (KS), Anderson-Darling (AD) test). Conduct p-value uniformity test.

I'm late to this but posting it just in case some one still visits this question looking for answers, I'm using Jasmine version 3.0 and as mentioned by @Patrizio Rullo you can use **toBeGreaterThanOrEqual/toBeLessThanOrEqual**.

It was added in version 2.5 as per release notes - https://github.com/jasmine/jasmine/blob/master/release_notes/2.5.0.md

For e.g.

expect(percent).toBeGreaterThanOrEqual(1,"This is optional expect failure message");

or

expect(percent).toBeGreaterThanOrEqual(1);

**P-values and significance tests (video),** Using the sample data and assuming the null hypothesis is true, calculate the value of the test statistic. Again, to conduct the hypothesis test for the population I want to know how to test whether an input value is an integer or not. I have tried using the function isinteger, but I obtain, for example, isinteger(3) = 0.Apparently, any constant is double-precision by Matlab default, and is therefore not recognized as an integer.

**Example calculating t statistic for a test about a mean (video),** Use indexOf to see if x is in an array. if([1,2].indexOf(x) !== -1). My advice is to weigh the costs for designing, preparing and running the A/B test against the potential benefits (with a reasonable future extrapolation, e.g. several years) and see the sample sizes (and thus time) required by several different combinations of values for the three main parameters (significance, power, minimum effect size). Then, chose a statistical design that hits closest to the perfect balance.

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**S.3.2 Hypothesis Testing (P-Value Approach),** The chance level is then used as the test value against which the sample mean of the test variable is compared. Statistical difference between a Interpreting the Overall F-test of Significance. Compare the p-value for the F-test to your significance level.If the p-value is less than the significance level, your sample data provide sufficient evidence to conclude that your regression model fits the data better than the model with no independent variables.