Data can be fake or manipulated though. And in the case of covid-19, a lot of the data is highly questionable.

"The figures are questionable Angelo Borrelli, the head of Civil Protection, who announces the latest figures every day at 6 p.m., said Saturday night that the 793 new deaths have been caused “by and with” the coronavirus. “We count all the dead, we make no distinction between with and by the coronavirus.” However, one wonders whether these daily figures reflect the situation correctly. The dead are said to have almost all had one or more other diseases, which leaves a question mark as to exactly how deadly the coronavirus is. At the same time, it has become clear that a large number of people who die at home (which is often a retirement home) do not undergo a coronavirus test." https://www.nrc.nl/nieuws/2020/03/22/als-italie-het-voorland...

That paragraph is questioning the conclusion: how deadly the virus is, not the data: how many people died and how.

Seeking an alternate explanation for the data, which is what scientists do, is not the same as denying the data, which is what zealots do.

It also questions the data, for example, the fact that even elderly dying at home without having been tested at all for COVID-19 (!!!) being counted as COVID-19 deaths. This is something I come across in more countries including the USA where various doctors have protested against policies that almost force them to include COVID-19 on death certificates. It's as if they want to artificially inflate the numbers to make COVID-19 seem a lot worse than it is. Watch this interview with Dr. Dan Erickson and Dr. Artin Massihi of Bakersfield, CA.: https://www.youtube.com/watch?v=cmjYRJXKixY

While it is academically interesting to try to apportion blame between COVID-19 and underlying conditions, the basic fact on the ground is an anomalous rise in illnesses requiring critical care and resulting in death.

This has been true since the first alarm bells in China, and has spread to other nations in a way that is easily observed first-hand, clearly reported by front-line medical staff, and consistent with the spread of a new disease.

That obvious data is what populations and governments are reacting to, not some calculated IFR.

> You can't "disprove" data.

In the interest of correctness, yes you can.

To disprove means "to prove to be false or wrong"[1], and that's exactly what the OPERA collaboration did to their own data that showed super-luminal neutrinos, when they later discovered it was caused by a bad fiber connection[2].

There's also the case of BICEP2[3], where the raw data was not in dispute, but rather the inevitable post-processing required to extract the "science signal" from the raw data.

This is of course why scientists prefer to have multiple independent measurements of the same phenomena, to eliminate possible sources of error and bias. The LHC for example has two independent main detectors[4], built by different teams using different approaches, for this very reason.

That said, at least in the physical sciences (I don't follow life sciences as closely), cases like OPERA and BICEP2 are exceptions.

[1]: https://www.merriam-webster.com/dictionary/disprove

[2]: https://en.wikipedia.org/wiki/Faster-than-light_neutrino_ano...

[3]: https://en.wikipedia.org/wiki/BICEP_and_Keck_Array#BICEP2

[4]: https://home.cern/science/experiments

Trying to label someone without engaging in critical thinking that involves understanding what you memorized is being dishonest to yourself. I will say it again. A theory is just a theory.. just the most accepted story.

Gravity is a theory (an explaination) of an observable phenonmenon. It is the accepted story until a more compelling story comes around.

Some popular theories that were disproven:

Fleischmann–Pons’s Nuclear Fusion

Phrenology

The Blank Slate

Luminiferous Aether

To your other point. Data is constantly being re-examined. How accurate is the Data is. What the data is really examining are extremely important and change.

Data is re-analyzed in the context of new data and improved hypotheses. Data is not "disproven" as you claimed. There may be several ways to explain an observation, and some of the explanations can be shown to be wrong, but the observation itself never can.

After the discovery of standard candles, when the map of the universe was completely reconceptualized, nothing ever changed about astronomical data: observations of stellar position and brightness. What had changed was the explanation for those brightnesses and the meanings attributed to them. The data stays the same, the explanation changes, and if an explanation doesn't match the data, like the milky-way-is-the-universe hypothesis, then it's a bad explanation.

What about false reporting?