When I was in Berlin last winter, I noticed a few coffee shops that had strict no-laptop policies at the front but often allowed laptops in back rooms or further away from the entrance. Seemed like a win-win.
Why is the rate change important? For two main reasons:
1) it signals the intention of the FED to actively use its tools to manage the growth of the economy as opposed to uncritically encouraging it;
2) even a token charge decreases the demand for what was once free and breaks a crucial psychological barrier for further increases in the future (see point 1; also, the freemium model in apps for an analogy).
So any change upwards really matter after nearly a decade of decreases and the zero-bound rates.
Basis points communicate the absolute change rather than the relative change to remove ambiguity. For example, consider a 1% interest rate: if it goes up by 100 BPS, the new rate is 2%; if by 1%, the new rate is 1.01%.
The article does not consider the most onerous and backwards provision debated as a part of the consultation: a mandatory 5 minute wait period between getting a car and start of the journey, even if the car is available immediately.
I'd recommend Irving Kirsch's __Emperor's new drugs: Exploring the antidepressant myth__ as an accessible introduction to this topic.
The book goes beyond statistics and draws attention to "clinical significance" that is crucial to keep in mind when evaluating drugs. Statistical significance helps to inform whether a metric has changed; clinical significance tells whether it actually mattered from a patient's perspective.
Dr. Kirsch's (a psychologist) work has been valuable, with some justified criticisms of antidepressant therapy and their trials. But his work did not show that antidepressants are clinically ineffective.
Dr. Kirsch's analysis showed that for mild depression treated with antidepressants, there is in fact an improvement in symptoms of depression based on rating scales like the Hamilton Depression Scale - but for mild depression, this improvement in the numbers doesn't necessarily translate into what you correctly identify as clinical significance. However, Dr. Kirsch's own "cutoff" for whether or not such a numerical increase constitutes "clinical significance" or not is itself just an arbitrary number that itself has been criticized.
Also, Kirsch's own metaanalyses (and other metaanalyses) show clearly that for moderate and severe depression (and other conditions), antidepressants are indeed clinically effective (in other words, yes, the benefits actually "mattered" from a patient's perspective), and, importantly, they are highly effective at preventing relapse.
They work, and work well.
In addition, there are a variety of reasons why many short-term clinical trials of antidepressants (which have made up the bulk of clinical trials of SSRIs, for example) are entirely different from the way physicians prescribe antidepressants (and other medications) clinically, and may underestimate their benefit even for cases of mild depression. The beneficial effects of antidepressants increase with longer exposure (i.e. exceeding the length of a six week clinical drug study), probably because of downstream neurotrophic factor(s) increases. Many trials of antidepressants limited dose adjustment or dose adjustment rate. Many clinical trials have excluded patients with severe symptoms or dual diagnoses because inclusion introduces a safety issue or may introduce too many variables. Also, physicians will switch patients to another antidepressant if there is no sign of early efficacy, and while one antidepressant may work very well (probably as a result of genetics), another will not work at all. Most studies focusing on one drug are not going to switch to another medication in the same class (for example, a serotonin reuptake inhibitor) during the trial, even though that's routinely done in clinical practice with good result.
Antidepressants work. Psychotherapy can also be helpful as a sole treatment for mild depression (although please note that psychotherapy has its own set of risks, costs, and drawbacks), and patients often get additional benefit by engaging in psychotherapy along with medication treatment. However, a psychologist or counselor treating a moderately to severely depressed person who fails to refer that patient for evaluation for treatment with an antidepressant would be risking a malpractice suit.
This is one of my most favorite essays and I think about it daily. I increasingly believe that modern workplace, with its emphasis on open space plans and team work, gradually develops groupthink that can be potentially destructive. [1]
It is not only the absorption of others' opinions. It is impossible to pause and critically evaluate thoughts for several minutes when not alone. Mistakes remain unidentified mainly because no one had a moment to discover them; conversely, they might get dismissed if their understanding requires larger amount of time. It is difficult to think for oneself. I work in a management/economic consultancy and see this every day.
How can "solitude" enter work?
[1] The Organization Man by William Whyte, a classic management book from the 50s, is a relevant resource for a critical analysis of a workplace along these lines.
The situation is reminiscent of the newspaper industry, particularly with respect to the significant contribution of a entity to the parent's bottom line. For their entire existence, newspapers have relied on lucrative content, eg, real estate or auto classifieds, cross-subsidizing the rest of the paper. As the internet and smartphone apps came along, the bundle fell apart and migrated into separate services online. New providers captured the lucrative markets and their revenues, removing the ability of newspapers to continue cross-subsidizing the actual news. [1]
I second the recommendation of Innovators. Even though it does not go into deep detail, it provides a good overview of the history of computing. It is easy to then follow-up with specific literature if a topic piques one's interest.
Assuming there is no inherent bias in terms of sentiment and vocabulary, one approach would be to repeatedly randomly sample 300 negative words from the corpus and generate a vector of sentiments. You could then average the elements of the vector to get an average sentiment, or use another metric from basic stats. That could decrease the bias.
