Sentences with phrase «get your data wrong»

Answer 1: They didn't get your data wrong.

If you think both NOAA and PSMSL have got their data wrong, you tell them.

Not exact matches

As Diamond says, «For every 1,000 men who get regularly screened for prostate cancer, about 20 % of them will end up getting unneeded biopsies or even have their prostate unnecessarily removed because the data is wrong.»

In the same way that a chef gets frustrated working with someone else's dull knives, data scientists will become equally frustrated if forced to use the wrong tools for the job.

A former member of the Bank of England (BOE) told CNBC it's «unwise» for the central bank to stick to a long - term policy strategy in case it gets wrong - footed by new economic data.

Managing complex AI technologies requires experts with PhDs and constant monitoring to keep the wrong kind of data from getting in, resulting in useless results.

(Don't get me wrong, they do put out some good data).

Now don't get me wrong, collecting and analyzing the wrong data is an absolute disaster.

Late Friday, confronted with mounting evidence that it was wrong, the Silicon Valley company tried to get ahead of media reports by putting out a blog post that it had received «reports» that Cambridge Analytica hadn't deleted the user data and that it had suspended the firm.

Often we are faced with disparate information, incomplete data, only parts of the puzzle rather than the whole, or hints and innuendo rather than verifiable fact, and then are required to make important investment decisions where the downside if we get it wrong can be quite painful.

But the data suggest that the market normally prices yields slightly above the economy's nominal growth rate, partially as insurance against getting the inflation forecast wrong.

One of the things that casual observers often get wrong is that my outlook isn't driven by some perma - bearish philosophy, but is instead a function of observable data — specifically, the historical relationship between observable data and subsequent outcomes in the financial markets and the economy.

You did per capita wrong even with using the wrong data is is the abortion number / population number not the other way around, then multiplied by 100k to get rid of the number.

You don't get much more biased than that... they won't even listen to any data that shows the bible is wrong.

Potential new bets were brainstormed, the staff guessing which teams would emerge from the championship games (inevitably getting one wrong) and starting their work on updating the data for this year.

«To say that the police can't get data from the internet without this bill is simply wrong.

It's an amazing feeling to not get stuck with patients, to be able to really think through what's going wrong with them, to not follow the protocol that you were taught if you see «adrenal fatigue» or whatever it is but to think through every single person that come through, to look at the data as a complete unique individual and like I said, it's such a great and empowering feeling to be able to help people that way that I want them to turn around and help others.

Personal data is kept confidential from getting into the wrong hands.

The No Child Left Behind Act imposes the wrong kind of testing on schools, educators need better systems to interpret the test data they get, and the federal government should help pay for the mandates it imposes, according to several advocates who last week addressed a private panel studying the education law and how to improve it.

If you ask the wrong question or you do not have enough or correct data, the answer you will get can never be what it should be and what exactly is expected.

Where McAfee and Brynjolfsson get it wrong is their belief that big data directly translates through managers to improved performance.

The average layperson who isn't reading about education policy and data all day might get the wrong impression from that statement.

Don't risk getting involved with the wrong kind of website where your personal data could be compromised or stolen.

A wrong thesis paper can make a huge loss in the career of researchers but by producing enough data analyzing, methodology, and knowledge, it is possible to get success with the thesis papers for your present and also for future.

I would like to be proven wrong, but for the time being, it seems that my dream of getting books from multiple bookstores on an e-ink device without the need to sideload and / or decrypt encrypted data is just that: a dream.

It will save you a great deal of hassle later if your financial data gets into the wrong hands.

Due in large part to the vast rise in identity theft, it's possible that your data could get into the wrong hands.

It is only allowed to request a checkup on what you believe to by wrong entries and if proved, you can request them to be removed free of charge and you can also request a re-consideration if you got declined due to mistaken data on your credit report.

But the data suggest that the market normally prices yields slightly above the economy's nominal growth rate, partially as insurance against getting the inflation forecast wrong.

My good friend Mike Piper has written an article («Investing Based on Market Valuation») at his Oblivious Investor blog exploring my finding that the Old School safe withdrawal rate studies get the numbers wildly wrong (promoted recently by my other good friend Todd Tresidder) and the research done by my other good friend Wade Pfau showing that Valuation - Informed Indexing has for the entire 140 years for which we have market data available to us provided far higher returns at greatly reduced risk.

This doesn't mean his model is wrong, but that the odds of it forecasting well in the future are lower because each model adjustment effectively relies on less data as the model gets «tuned» to eliminate past inaccuracies.

Don't get me wrong I know the data entered into the model is intense and has to bear a lot of scrutiny but with hackers and binary knowledge of computers there is no way I could be convinced that a model couldn't be manipulated by an Expert Programmer.

It gets before 2000 wrong and adding post 2000 data to it produces a radically different number.

Watch the data with me each day of each year as Dressler gets more and more wrong.

But we hear over and over again, «it hasn't changed, your theories are wrong, the reefs is in great shape, your data are bogus, you spend all your time in a lab in a city, etc.» And you know what; it gets frustrating.

Versus Michael Mann's hockey stick showing there was no enigmatic medieval period (even tried to change the name) with greenhouse gases emerging as the dominant forcing in the twentieth century — but was based on incredible data - selection techniques and was mostly based on one tree core series, the bristlecone pine trees from one mountain which can not possibly be expected to provide a reliable indicator of climate — the worst type of science but still accepted by climate science because that it what they do — rewrite history and get all the facts wrong.

But the ALARMISTS got even non-controversial data wrong, and replaced it only when alerted by a «denier».

Don't get me wrong, models are an important part of defining the expectations, but they are just that, an expectation with little supporting data yet.

I'm not quite sure how you've got less than 500ppmv, for CO2 concentrations, on current trends, by the end of the century but if you taken the trouble to plot out the data you'd see you'd got the wrong answer.

The data would indicate that you've got it right (on the purported ocean «acidification») and Webby is wrong.

They even get the description of the error wrong (they say the Nov data was a duplication of Oct, but it was Oct / Sept).

The raw data are not independent; they get that wrong, and all 3 «sources» are wrong from the beginning.

Why don't you two get together and collaborate on a paper that studies the issues raised by Greg and if / where he is wrong, put the issues to bed, and, conversely, if / where he is right, his criticism will have produced a valuable improvement to the data set.

I got my bias calculation wrong because of lack of accurate meta data it should have been applied 20 year earlier / not at all.

I suspect that any series that doesn't go back to about 1000 AD, or else ends early, say 1950 has been pruned to get rid of the «wrong» data.

You are making basic mathematical errors and getting your descriptions of your own data hopelessly wrong.

The observed data proves that CAGW is wrong, and as we get more and more data, this is becoinmg ever more clear.

One result is that worthless data gets treated as real and is used to draw wrong conclusions about temperature history.

Consensus was wrong in this case where the system is simpler and it's possible to get good data and do real experiments.

If you get the «wrong» answer (whatever that means) you can't go back and add an adjustment factor to the data you measured.