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An example of hypothesis is the theory of evolution.

The American Heritage Dictionary defines a as, "a tentative explanation for an observation, phenomenon, or scientific problem that can be tested by further investigation." This means a hypothesis is the stepping stone to a soon-to-be proven theory. For a hypothesis to be considered a scientific hypothesis, it must be proven through the scientific method. Like anything else in life, there are many paths to take to get to the same ending. Let's take a look at the different types of hypotheses that can be employed when seeking to prove a new theory.

A statistical hypothesis is an examination of a portion of a population.

A logical hypothesis is a proposed explanation possessing limited evidence. Generally, you want to turn a logical hypothesis into an empirical hypothesis, putting your theories or postulations to the test.

In order for a hypothesis to be sound, hold tight to these tips:

Make sure your hypothesis is testable with research and experimentation.

As you may have learned last month, Boost is up to some exciting things! Previously, we discussed using lexical analysis as a tool to identify words or phrases that have a positive or negative impact on your results. This month we will address hypothesis testing and putting the lexical analysis to work.

Stephen Krashen developed 5 hypotheses on language acquisition: The Acquisition-Learning Distinction, The Input Hypothesis, The Monitor Hypothesis, The Nature Hypothesis, and The Affective-Filter Hypothesis.

Gaetano Squadrito Chairman of HYPOTHESIS XII

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An empirical hypothesis, or working hypothesis, comes to life when a theory is being put to the test, using observation and experiment. It's no longer just an idea or notion. It's actually going through some trial and error, and perhaps changing around those independent variables.

If you wanted to conduct a study on the life expectancy of Savannians, you would want to examine every single resident of Savannah. This is not practical. Therefore, you would conduct your research using a statistical hypothesis, or a sample of the Savannian population.

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  • an unproved theory, proposition, supposition, etc

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  • Hypothesis Synonyms, Hypothesis Antonyms | …

    Important paper: Fama (1970)

  • Dictionary and Word of the Day.

    Broken down into (somewhat) English, that’s H1 (The hypothesis): μ (the average) (is greater than) 8.2

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Every scenario the computer runs, someone had to write by hand

"For the CAPM or the multifactor APT to be true, markets must be efficient."
"Asset-pricing models need the EMT. However, the notion of an efficient market is not affected by whether any particular asset-pricing theory is true. If investors preferred stocks with a high unsystematic risk, that would be fine: as long as all information was immediately reflected in prices, the EMT theory would be true."
Lofthouse (2001), page 91

History: The Formation of the Endosymbiotic Hypothesis

One of the reasons for this state of affairs is the fact that the EMH, by itself, is not a well-defined and empirically refutable hypothesis. To make it operational, one must specify additional structure, e.g. investors' preferences, information structure. But then a test of the EMH becomes a test of several auxiliary hypotheses as well, and a rejection of such a joint hypothesis tells us little about which aspect of the joint hypothesis is inconsistent with the data. Are stock prices too volatile because markets are inefficient, or is it due to risk aversion, or dividend smoothing? All three inferences are consistent with the data. Moreover, new statistical tests designed to distinguish among them will no doubt require auxiliary hypotheses of their own which, in turn, may be questioned."
Lo in Lo (1997), page

Hypothesis Testing - Kean University

"One of the reasons for this state of affairs is the fact that the Efficient Markets Hypothesis, by itself, is not a well-defined and empirically refutable hypothesis. To make it operational, one must specify additional structure, e.g., investor’ preferences, information structure, business conditions, etc. But then a test of the Efficient Markets Hypothesis becomes a test of several auxiliary hypotheses as well, and a rejection of such a joint hypothesis tells us little about which aspect of the joint hypothesis is inconsistent with the data. Are stock prices too volatile because markets are inefficient, or is it due to risk aversion, or dividend smoothing? All three inferences are consistent with the data. Moreover, new statistical tests designed to distinguish among them will no doubt require auxiliary hypotheses of their own which, in turn, may be questioned."
Lo and MacKinlay (1999), pages 6-7

How to Plan and Write a Testable Hypothesis - wikiHow

"The notion of market efficiency is not a well-posed and empirically refutable hypothesis. To make it operational, one must specify additional structure, e.g., investors’ preferences, information structure, etc. But then a test of market efficiency becomes a test of several auxiliary hypotheses as well, and a rejection of such a joint hypothesis tells us little about which aspect of the joint hypothesis is inconsistent with the data."
Lo (2000) in Cootner (1964), page x

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