Contradictory Hypotheses
Contradictory Hypotheses - Related Videos
01:16
Null and Alternative Hypotheses
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2023
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 population that is...
01:38
Cognitive Dissonance
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2020
Social psychologists have documented that feeling good about ourselves and maintaining positive self-esteem is a powerful motivator of human behavior (Tavris & Aronson, 2008). In the United States, members of the predominant culture typically think very highly of themselves and view themselves as good people who are above average on many desirable traits (Ehrlinger, Gilovich, & Ross, 2005). Often, our behavior, attitudes, and beliefs are affected when we experience a threat to our...
07:23
Scientific Method - Concepts
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2019
The scientific method is used to solve problems and explain phenomena. The development of the scientific method coincided with changes in philosophy underpinning scientific discovery, radically transforming the views of society about nature. During the European Renaissance, individuals such as Francis Bacon, Galileo, and Isaac Newton formalized the concept of the scientific method and put it into practice. Although the scientific method has been revised since its early conceptions, much of the...
06:57
Animal Diversity - Student Protocol
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2019
Form and Function of Crickets and Crayfish ExpandNOTE: To observe the relationship between form and function in animals, you will be examining and dissecting a cricket and a crayfish. Each pair of students will receive one of each of these organisms. When dissecting, be sure to wear gloves, closed-toed shoes, and safety goggles. Take care when handling dissecting tools, as they may be sharp. HYPOTHESES: In this experiment, the experimental hypothesis could be that the cricket and crayfish have...
06:58
The Factorial Experiment
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2023
Source: Laboratories of Gary Lewandowski, Dave Strohmetz, and Natalie Ciarocco—Monmouth University A factorial design is a common type of experiment where there are two or more independent variables. This video demonstrates a 2 x 2 factorial design used to explore how self-awareness and self-esteem may influence the ability to decipher nonverbal signals. This video leads students through the basics of a factorial design including, the nature of a factorial design and what distinguishes it from...
FAQs
The null hypothesis supplies the expected distribution against which observed data are evaluated. A test statistic summarizes a relevant feature of the data, and its position relative to that null distribution indicates how unusual the observation would be if the null model were used as the reference. This comparison creates a consistent basis for assessing competing claims.
A test statistic converts observed sample information into a value that can be compared with the distribution expected under the null hypothesis. Its location within that distribution helps determine whether the data provide sufficient evidence for rejection. The statistic therefore connects the raw observations to the formal decision rule without treating the observations alone as conclusive.
A p-value helps assess how the observed result relates to the distribution expected under the null hypothesis. When the result provides sufficient evidence according to the selected decision standard, researchers may reject the null in favor of the contradictory alternative. The value supports an evidence-based decision, but it does not establish the alternative as universally proven.
Failure to reject means that the observed data did not provide sufficient evidence against the null under the chosen testing framework. It does not show that the null claim has been confirmed or that contradictory possibilities have been eliminated. This distinction prevents researchers from treating limited or inconclusive evidence as proof of a population or relationship claim.
Researchers first state the competing claims about a population, parameter, or relationship, then collect relevant observations and calculate a test statistic. They compare that statistic with the distribution expected under the null and examine a p-value or confidence interval. The final interpretation states whether the evidence is sufficient to reject the null, rather than declaring it proven when rejection does not occur.
A confidence interval provides an additional way to evaluate the claim about a population parameter or relationship alongside formal hypothesis testing. Its reported range helps researchers judge the values compatible with the data within the stated statistical framework. Used with the decision about the null hypothesis, it can clarify the evidence rather than reducing the conclusion to a simple reject-or-not outcome.
This framework supports decisions in experiments, clinical studies, quality control, and scientific model evaluation. In each setting, researchers compare observed results with expectations generated by the null claim, then determine whether the evidence justifies rejection. The same logic helps organize conclusions about populations, parameters, or relationships while preserving the distinction between insufficient evidence and proof.