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Related Concept Videos

Statistical Hypothesis Testing01:16

Statistical Hypothesis Testing

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Hypothesis testing is a critical statistical procedure facilitating informed, evidence-based decisions. It begins with a hypothesis, which is a tentative explanation, or a prediction about a population parameter. This hypothesis can be either a null hypothesis (H0), indicating no effect or difference, or an alternative hypothesis (Ha), suggesting an effect or difference.
Statistical significance measures the probability that an observed result occurred by chance. If this probability, known as...
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Decision Making: Traditional Method01:14

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The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
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Types of Hypothesis Testing01:11

Types of Hypothesis Testing

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There are three types of hypothesis tests: right-tailed, left-tailed, and two-tailed.
When the null and alternative hypotheses are stated, it is observed that the null hypothesis is a neutral statement against which the alternative hypothesis is tested. The alternative hypothesis is a claim that instead has a certain direction. If the null hypothesis claims that p = 0.5, the alternative hypothesis would be an opposing statement to this and can be put either p > 0.5, p < 0.5, or p...
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Hypothesis Test for Test of Independence01:16

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The test of independence is a chi-square-based test used to determine whether two variables or factors are independent or dependent. This hypothesis test is used to examine the independence of the variables. One can construct two qualitative survey questions or experiments based on the variables in a contingency table. The goal is to see if the two variables are unrelated (independent) or related (dependent). The null and alternative hypotheses for this test are:
H0: The two variables (factors)...
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What is a Hypothesis?01:14

What is a Hypothesis?

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A hypothesis can be a simple sentence or statement about a property or any phenomenon observed or predicted for a population. It is usually a claim about a  property of the population. It can be stated for any field observations or experiments. A hypothesis statement cannot be said to be right or wrong as it is merely a statement. It needs to be tested through an elaborate data collection process and an appropriate statistical test. A hypothesis should be a general but not a vague...
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Null and Alternative Hypotheses01:16

Null and Alternative Hypotheses

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The actual hypothesis testing begins by considering two hypotheses. They are termed  the null hypothesis and the alternative hypothesis. These hypotheses contain opposing viewpoints.
The null hypothesis, denoted by H0 is a statement of no difference between the variables—they are not related. This can often be considered the status quo. As  a result if you cannot accept the null, it requires some action.
The alternative hypothesis, denoted by H1 or Ha, is a claim about the...
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Barnes Maze Testing Strategies with Small and Large Rodent Models
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A simple, general, and efficient method for sequential hypothesis testing: The independent segments procedure.

Jeff Miller1, Rolf Ulrich2

  • 1Department of Psychology, University of Otago.

Psychological Methods
|October 5, 2020
PubMed
Summary
This summary is machine-generated.

A new sequential hypothesis testing method uses independent data segments, often reducing sample sizes by 30%. This efficient procedure is simpler and more flexible than existing sequential methods.

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Area of Science:

  • Statistics
  • Hypothesis Testing

Background:

  • Traditional fixed-sample hypothesis testing can be inefficient.
  • Existing sequential methods offer improvements but have limitations.

Purpose of the Study:

  • To introduce a novel sequential hypothesis testing procedure using independent data segments.
  • To enhance the efficiency and flexibility of statistical testing.

Main Methods:

  • Data collection and analysis in independent segments.
  • Allows for setting a desired overall alpha (α) level.
  • Applicable to a wide range of statistical tests.

Main Results:

  • Generally requires smaller sample sizes (approx. 30% reduction) compared to fixed-sample procedures.
  • Maintains desired alpha level and statistical power.
  • Offers advantages in simplicity and fewer assumptions over other sequential methods.

Conclusions:

  • The independent segments procedure is a simpler, more flexible, and efficient alternative for statistical testing.
  • This method can increase the efficiency of hypothesis testing in various circumstances.