Positive accounting theory hypothesis - think-forex …

Here are three experiments to illustrate when the different approaches to statistics are appropriate. In the first experiment, you are testing a plant extract on rabbits to see if it will lower their blood pressure. You already know that the plant extract is a diuretic (makes the rabbits pee more) and you already know that diuretics tend to lower blood pressure, so you think there's a good chance it will work. If it does work, you'll do more low-cost animal tests on it before you do expensive, potentially risky human trials. Your prior expectation is that the null hypothesis (that the plant extract has no effect) has a good chance of being false, and the cost of a false positive is fairly low. So you should do frequentist hypothesis testing, with a significance level of 0.05.

Making a positive change with the hypothesis-driven process

Now instead of testing 1000 plant extracts, imagine that you are testing just one. If you are testing it to see if it kills beetle larvae, you know (based on everything you know about plant and beetle biology) there's a pretty good chance it will work, so you can be pretty sure that a P value less than 0.05 is a true positive. But if you are testing that one plant extract to see if it grows hair, which you know is very unlikely (based on everything you know about plants and hair), a P value less than 0.05 is almost certainly a false positive. In other words, if you expect that the null hypothesis is probably true, a statistically significant result is probably a false positive. This is sad; the most exciting, amazing, unexpected results in your experiments are probably just your data trying to make you jump to ridiculous conclusions. You should require a much lower P value to reject a null hypothesis that you think is probably true.

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Most formal hypotheses connect concepts by specifying the expected relationships between propositions.

In the midst of racial segregation in the U.S.A and the ‘Jim Crow Laws’, Gordon Allport (1954) proposed one of the most important social psychological events of the 20th century, suggesting that contact between members of different groups (under certain conditions) can work to reduce and . Indeed, the idea that contact between members of different groups can help to reduce and improve social relations is one that is enshrined in policy-making all over the globe. UNESCO, for example, asserts that contact between members of different groups is key to improving social relations. Furthermore, explicit policy-driven moves for greater contact have played an important role in improving social relations between races in the U.S.A, in improving relationships between Protestants and Catholics in Northern Ireland, and encouraging a more inclusive society in post-Apartheid South Africa. In the present world, it is this of the benefits of contact that drives modern school exchanges and cross-group buddy schemes. In the years since Allport’s initial , much research has been devoted to expanding and exploring his . In this article I will review some of the vast literature on the role of contact in reducing , looking at its success, mediating factors, recent theoretical extensions of the hypothesis and directions for future research. Contact is of utmost importance in reducing and promoting a more tolerant and integrated society and as such is a prime example of the real life applications that psychology can offer the world.

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The significance level (also known as the "critical value" or "alpha") you should use depends on the costs of different kinds of errors. With a significance level of 0.05, you have a 5% chance of rejecting the null hypothesis, even if it is true. If you try 100 different treatments on your chickens, and none of them really change the sex ratio, 5% of your experiments will give you data that are significantly different from a 1:1 sex ratio, just by chance. In other words, 5% of your experiments will give you a false positive. If you use a higher significance level than the conventional 0.05, such as 0.10, you will increase your chance of a false positive to 0.10 (therefore increasing your chance of an embarrassingly wrong conclusion), but you will also decrease your chance of a false negative (increasing your chance of detecting a subtle effect). If you use a lower significance level than the conventional 0.05, such as 0.01, you decrease your chance of an embarrassing false positive, but you also make it less likely that you'll detect a real deviation from the null hypothesis if there is one.

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You must choose your significance level before you collect the data, of course. If you choose to use a different significance level than the conventional 0.05, people will be skeptical; you must be able to justify your choice. Throughout this handbook, I will always use P If you are doing an experiment where the cost of a false positive is a lot greater or smaller than the cost of a false negative, or an experiment where you think it is unlikely that the alternative hypothesis will be true, you should consider using a different significance level.