Gregor Mendels Experiments on Plant Hybrids: A Guided Study (Masterworks of Discovery)

Epub Gregor Mendel\'s Experiments On Plant Hybrids: A Guided Study (Masterworks Of Discovery) 1993

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Results have been mixed in equally random patterns.

The slowdown of the music industry and media has not been matched in the publishing industry and film. The failed twitter revolution of Iran had quite a few things in common with the Arab spring, but even in the Maghreb the results differed wildly. Social networks have impacted societies sometimes in exact opposites—while the fad of social networks have resulted in cultural and isolation in Western society, it has created a counterforce of collective communication against the strategies of the Chinese party apparatus to isolate its citizenry from within.

Most of these phenomena have been only observed, not explained by now. It is mostly in hindsight that a linear narrative is constructed, if not imposed on. The inability of many of the greatest digital innovations like viral videos or social networks to be monetized are just one of many proofs how difficult it is to get a comprehensive grasp on digital history.

Moore's law and its numerous popular applications to other fields of progress thus create an illusion of predictability in the least predictable of all fields—the course of history. These errors of reasoning will be amplified, if Moore's Law is allowed to come to its natural end. Peak theories have become the lore of cultural pessimism.

If Moore's law is allowed to become a finite principle, digital progress will be perceived as a linear progression towards a peak and an end. Neither will become a reality, because the digital is not a finite resource, but an infinite realm of mathematical possibilities reaching out into the analog world of sciences, society, economics and politics.

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Because this progress has ceased to depend on quantifiable basis and on linear narratives it will not be brought to a halt, not even slowed down, if one of its strains comes to an end. Moore's will create the disillusionment of a finite nature of the digital. It will become as popular as its illusion of predictability. After all there have bee no loonies carrying signs saying "The End is Not Near". In the late summer of , as European civilization began its extended suicide, dissenters were scarce.

On the contrary: From every major capital, we have jerky newsreel footage of happy crowds, cheering in the summer sunshine. More war and oppression followed in subsequent decades, and there was never a shortage of willing executioners and obedient lackeys. By mid-century, the time of Stalin and Mao and their smaller-bore imitators, it seemed urgent to understand why people throughout the 20 th century had failed to rise up against masters who sent them to war, or to concentration camps, or to the gulag.

So social scientists came up with an answer, which was then consolidated and popularized into something every educated person supposedly knows: People are sheep—cowardly, deplorable sheep. This idea, that most of us are unwilling to "think for ourselves," instead preferring to stay out of trouble, obey the rules, and conform, was supposedly established by rigorous laboratory experiments.

Worse yet, it's rampant in the conversation of educate laypeople—politicians, voters, government officials. Yet it is false. It makes for bad assumptions and bad policies. It is time to set it aside. Some years ago, the psychologists Bert Hodges and Anne Geyer examined one of Asch's own experiments from the s.

He'd asked people to look at a line printed on a white card and then tell which of three similar lines was the same length. Each volunteer was sitting in a small group, all of whose other members were actually collaborators in the study, deliberately picking wrong answers. Asch reported that when the group chose the wrong match, many individuals went along, against the evidence of their own senses. But the experiment actually involved 12 separate comparisons for each subject, and most did not agree with the majority, most of the time. In fact, on average, each person agreed three times with the majority, and insisted on his own view nine other times.

To make those results all about the evils of conformity is to say, as Hodges and Geyer note, that "an individual's moral obligation in the situation is to 'call it as he sees it' without consideration of what others say.

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To explain their actions, the volunteers didn't indicate that their senses had been warped or that they were terrified of going against consensus. Instead, they said they had chosen to go along that one time.

It's not hard to see why a reasonable person would do so. The "people are sheep" model sets us up to think in terms of obedience or defiance, dumb conformity versus solitary self-assertion to avoid being a sheep, you must be a lone wolf. It does not recognize that people need to place their trust in others, and win the trust of others, and that this guides their behavior. Stanley Milgram's famous experiments, where men were willing to give severe shocks to a supposed stranger, are often cited as Exhibit A for the "people are sheep" model.

But what these studies really tested was the trust the subjects had in the experimenter. Indeed, questions about trust in others—how it is won and kept, who wins it and who doesn't—seem to be essential to understanding how collectives of people operate, and affect their members. What else is at work?

It appears that behavior is also susceptible to the sort of moment-by-moment influences that were once considered irrelevant noise for example, divinity students in a rush were far less likely to help a stranger than were divinity students who were not late, in an experiment performed by John M. Darley and Dan Batson. And then there is mounting evidence of influences that discomfit psychologists because there doesn't seem to be much psychology in them at all.

For example, Neil Johnson of the University of Miami and Michael Spagat of University College London and their colleagues have found the severity and timing of attacks in many different wars different actors, different stakes, different cultures, different continents adheres to a power law.

If that's true, then an individual fighter's motivation, ideology, and beliefs make much less difference than we think for the decision to attack next Tuesday. Or, to take another example, if as Nicholas Christakis' work suggests, your risks of smoking, getting an STD, catching the flu or being obese depend in part on your social network ties, then how much difference does it make what you, as an individual, feel or think?

Perhaps the behavior of people in groups will eventually be explained as a combination of moment-to-moment influences like waves on the sea and powerful drivers that work outside of awareness like deep ocean currents. All the open questions are important and fascinating. But they're only visible after we give up the simplistic notion that we are sheep. It is a commonly held but erroneous belief that a larger study is always more rigorous or definitive than a smaller one, and a randomized controlled trial is always the gold standard.

However, there is a growing awareness that size does not always matter and a randomized controlled trial may introduce its own biases. We need more creative experimental designs. In any scientific study, the question is: "What is the likelihood that observed differences between the experimental group and the control group are due to the intervention or due to chance?

A randomized controlled trial RCT is based on the idea that if you randomly-assign subjects to an experimental group that receive an intervention or to a control group that does not, then any known or unknown differences between the groups that might bias the study are as likely to affect one group as another.

While that sounds good in theory, in practice a RCT can often introduce its own set of biases and thus undermine the validity of the findings.

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For example, a RCT may be designed to determine if dietary changes may prevent heart disease and cancer. Investigators identify patients who meet certain selection criteria, e. When they meet with prospective study participants, investigators describe the study in great detail and ask, "If you are randomly-assigned to the experimental group, would you be willing to change your lifestyle?

However, if that patient is subsequently randomly-assigned to the control group, it is likely that this patient may begin to make lifestyle changes on their own, since they have already been told in detail what these lifestyle changes are. If they're studying a new drug that only is available to the experimental group, then it is less of an issue. But in the case of behavioral interventions, those who are randomly-assigned to the control group are likely to make at least some of these changes because they believe that the investigators must think that these lifestyle changes are worth doing or they wouldn't be studying them.

Or, they may be disappointed that they were randomly-assigned to the control group, and so they are more likely to drop out of the study, creating selection bias. Also, in a large-scale RCT, it is often hard to provide the experimental group enough support and resources to be able to make lifestyle changes. As a result, adherence to these lifestyle changes is often less than the investigators may have predicted based on earlier pilot studies with smaller groups of patients who were given more support.

The net effect of the above is to a reduce the likelihood that the experimental group will make the desired lifestyle changes, and b increase the likelihood that the control group will make similar lifestyle changes. This reduces the differences between the groups and makes it less likely to show statistically significant differences between them.

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As a result, the conclusion that the intervention had no significant effect may be misleading. This is known as a "type 2 error" meaning that there was a real difference but these design issues obscured the ability to detect them. That's just what happened in the Women's Health Initiative study, which followed nearly 49, middle-aged women for more than eight years. The women in the experimental group were asked to eat less fat and more fruits, vegetables, and whole grains each day to see if it could help prevent heart disease and cancer. The women in the control group were not asked to change their diets.

However, the experimental group participants did not reduce their dietary fat as recommended—over 29 percent of their diet was comprised of fat, not the study's goal of less than 20 percent. Also, they did not increase their consumption of fruits and vegetables very much. In contrast, the control group reduced its consumption of fat almost as much and increased its consumption of fruits and vegetables, diluting the between-group differences to the point that they were not statistically significant.

The investigators reported that these dietary changes did not protect against heart disease or cancer when the hypothesis was not really tested. Paradoxically, a small study may be more likely to show significant differences between groups than a large one.

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The Women's Health Initiative study cost almost a billion dollars yet did not adequately test the hypotheses. A smaller study provides more resources per patient to enhance adherence at lower cost. Also, the idea in RCTs that you're changing only one independent variable the intervention and measuring one dependent variable the result is often a myth.

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