Null hypothesis
A null hypothesis is a designated baseline statistical claim, typically asserting that an effect, difference, or association is absent.
Statistics Every Programmer Needs
A null hypothesis is a specified default claim about a population or data-generating process that a hypothesis test evaluates evidence against.
- How it is used
- Provides the default claim against which p-values guide regression and distribution-testing decisions.
- How deep it goes
- Defines test-specific claims and interprets p-values against a chosen threshold, without developing a general theory of hypothesis testing.
- Examples
- Uses running-time regression, raisin classification, and Benford leading-digit counts to give distinct null claims.
- What you can do after
- Apply it via regression or chi-square p-values to decide whether to reject a stated claim at a predetermined threshold.
Read how Statistics Every Programmer Needs explains null hypothesis →
Statistics Slam Dunk
A null hypothesis is a statistical proposition describing a baseline condition such as no effect, difference, or association.
- How it is used
- Frames significance tests as decisions about whether observed differences are due to chance.
- How deep it goes
- Explains baseline claims, p-value thresholds, rejection decisions, and t-test inputs without formal proofs.
- Examples
- Uses NBA home-road fouls and free throws, salary Gini coefficients, collections treatments, drugs, exam scores, and branch-service ratings.
- What you can do after
- Apply it via R's t.test to decide whether two-group differences meet a predefined significance threshold.
Grokking Statistics
A hypothesis stating that a claimed population value or relationship represents the status quo to be evaluated.
- How it is used
- Frames claims about population means, directional differences, and linear correlation for hypothesis testing.
- How deep it goes
- Defines H0, contrasts it with Ha, and applies rejection rules without deriving the underlying test proofs.
- Examples
- Uses soda volumes, tea-order guessing, 142-gram chip bags, and sports-drink sales versus temperature.
- What you can do after
- Apply it via SciPy to state H0, calculate a test result, and reject or fail to reject it at a chosen threshold.
Causal AI
A null hypothesis is a statistical claim, typically asserting the absence of an effect or association, that is evaluated by a hypothesis test.
- How it is used
- Frames statistical decisions about conditional independence and causal-model refutation through competing claims and p-value thresholds.
- How deep it goes
- Explains hypothesis direction, p-values, significance levels, and causal-testing limitations, but stops short of deriving test statistics.
- Examples
- Uses a transportation DAG, chi-squared tests, d-separation assertions, stock-price patterns, and DoWhy refuters.
- What you can do after
- Apply it via pgmpy or DoWhy to test conditional independences and assess whether causal assumptions are refuted.
Related concepts
- alternative hypothesiscontrasts with · An alternative hypothesis is a statistical proposition that contrasts with the null hypothesis by asserting an effect, difference, relationship, or other departure.
- false positiveused by · A false positive is a result that incorrectly indicates a condition or class is present when it is absent.
- p-valuemeasured by · A p-value is the probability, assuming a specified null hypothesis, of obtaining a test statistic or result at least as extreme as the observed one.
- hypothesiskind of · A hypothesis is a provisional proposition, explanation, or prediction that can be evaluated against evidence.
- hypothesis testingused by · Hypothesis testing is a formal statistical procedure that uses observed data to evaluate evidence about a specified hypothesis, often a null hypothesis.
- frequentist hypothesis testused by · A frequentist hypothesis test evaluates evidence using the long-run sampling behavior of data under a null hypothesis, without assigning probabilities to hypotheses.
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