Hypothesis Flashcards
Introduced by sir Ronald Fisher, Jerzy Newman, Karl Pearson and Egon Pearson
Hypothesis
Statistical method that is used in making statistical decisions using experimental data.
Hypothesis
[3] Methods of Test Hypothesis
- Traditional method
- P-value method
- Confidence interval method
[4] Scientific Method
- Observation
- Hypothesis
- Data gathering
- Data analysis
Statistical hypothesis is an assumption or claim about the population parameter.
[scientific method]
Hypothesis
Collection of evidences to prove or disprove the claim.
[scientific method]
Data gathering
Processing the evidence to give meaning or significance.
[scientific method]
Data analysis
A statistical hypothesis is a conjecture about the population parameter. This conjecture may or may not be true.
Statistical Hypothesis
[2] Types of Statistical Hypothesis1
- Null hypothesis
- Alternative hypothesis
currently accepted / Established thing.
[statistical hypothesis]
Null hypothesis
Always opposite of Null, involves the claim to be tested.
[statistical hypothesis]
Alternative hypothesis
It’s a statistical hypothesis testing that assumes that the observation is due to a chance factor.
[statistical hypothesis]
Null hypothesis
Currently accepted fact, contrary to the claim that is YET to be proven.
[statistical hypothesis]
Null hypothesis
Expressed with negative tone statement.
[statistical hypothesis]
Null hypothesis
It is the opposite of the null hypothesis; it shows that observations are the result of a real effect. It states that there is a difference between two population means (or parameters).
[statistical hypothesis]
Alternative hypothesis
Aka RESEARCH HYPOTHESIS.
[statistical hypothesis]
Alternative hypothesis
Statement/claim that is YET TO BE PROVEN!
[statistical hypothesis]
Alternative hypothesis
Expressed with positive tone hypothesis.
[statistical hypothesis]
Alternative hypothesis
It shows that the null hypothesis should be rejected when test value is in the critical region on one side of the mean.
[one/two tailed]
One tailed test
It may be either a right tailed or left tailed test, depending on the direction of the inequality of the alternative hypothesis.
[one/two tailed]
One tailed test
It shows that the null hypothesis should be rejected the test value is in either of the two critical regions.
[one/two tailed]
Two-tailed test
[2] Decision making errors
- Type 1 error
- Type 2 error
Error of rejecting a true null hypothesis.
[decision making errors]
Type I error (a)
When it is important not to make a mistake of rejecting a true H0.
[decision making errors]
Type 1 error (a)
Probability of rejecting the null hypothesis, when in fact the null hypothesis is true.
[decision making errors]
Type I error (a)
AKA Alpha Error.
[decision making errors]
Type I error (a)
In simple explanation: rejects null when null is in fact correct.
[decision making errors]
Type II error (b)
Probability of not rejecting a false null hypothesis.
[decision making errors]
Type II error (b)
It is important not to accept a false H0.
[decision making errors]
Type II error (b)
Related to the type I error.
[decision making errors]
Type II error (b)
In simple explanation: do not reject the null when null is in fact wrong.
[decision making errors]
Type II error (b)
[2] Critical value
- Critical region
- Non-critical region
Also known as REJECTION REGION or alpha region.
[critical value]
Critical region
Range of the values of the test value that indicates that there is significant difference and that the null hypothesis should be rejected.
[critical value]
Critical region
Favors the alternative hypothesis.
[critical value]
Critical region
Also known as NON-REJECTION REGION or beta region.
[critical value]
Non-critical region
Range of values of the test value that indicates that the difference was probably due to change and the null hypothesis should not be rejected.
[critical value]
Non-critical region
Favors the null hypothesis.
[critical value]
Non-critical region
