sham data

USAGE SUMMARY

The phrase "sham data" is correct and usable in written English.
It can be used to refer to data that is false, misleading, or not genuine, often in contexts related to research or analysis. Example: "The study was discredited after it was revealed that the researchers had used sham data to support their conclusions."

✓ Grammatically correct

Science

Human-verified examples from authoritative sources

Exact Expressions

12 human-written examples

The different values are based on the fact that the analysed brain region is exactly the same for both glioma rats and control rats, according to the general expression: % Δ Data = Data glioma − Data sham Data sham × 100 (4).

EJNMMI Research

The student's t-test was used to make comparisons between time-matched Crush and Sham data.

Plosone

For the sake of completeness, the trendline formulas for ANGLE and COLOR SHAM data separately were RT = 5.66*TW+525.6 [for ANGLE] and RT = 6.99*TW+502.14 [for COLOR]).

Plosone

We fitted a linear trendline to the average SHAM data (averaged between the two tasks for each time window), and moved the intercept of this trendline so that it fell exactly between the ANGLE and the COLOR TC values.

Plosone

The histomorphometric analysis of the femur mid-diaphysis region revealed that thermal trauma decreased the cortical area 2.6% when compared to sham, and the average and minimum cortical widths by 7.5% and 10% respectively; accordingly, the marrow space was increased by 6.4% compared to sham (data not shown).

Plosone

Each group was then adjusted against corresponding sham data to establish relative protein expression when compared with sham animals.

DMM Disease Models and Mechanisms

Human-verified similar examples from authoritative sources

Similar Expressions

48 human-written examples

72 h age-matched, uninjured animals displayed contrast enhancement profiles similar to those of shams (data not shown).

Plosone

SOD inhibition induced a decrease in renal blood flow at 48 h after sepsis induction, but not at 12 and 24 h (Fig. 7a) and not in sham animals (data not shown).

Intensive Care Medicine Experimental

Seven days after reperfusion, serum creatinine in control and nicotine pre-treated groups was comparable to baseline creatinine levels measured in sham animals (data not shown).

Plosone

By comparing the sporadic pattern in wild-types (Figure 2D) or the DMSO sham control (data not shown), it was noted that foxi3a expression in DAPT-treated embryos had become about 2.2-fold denser and more homogeneous (Figure 2E; also summarized in Table 1).

Plosone

Proliferation assay showed no significant difference between OVX and Sham MSCs (data not shown).

Cell Death and Disease

Expert writing Tips

Best practice

When using "sham data", ensure you clearly articulate the reason for its use (e.g., as a control in an experiment) or the context in which it was identified as being false or misleading.

Common error

Avoid assuming that all deviations from expected results automatically indicate "sham data". Thoroughly investigate potential sources of error before labeling data as such.

Linguistic Context

The phrase "sham data" primarily functions as a noun phrase, where "sham" acts as an adjective modifying the noun "data". Ludwig AI confirms this is usable in English. Examples from Ludwig show it describing data that is not genuine or valid.

Expression frequency: Common

Frequent in

Science

100%

Less common in

News & Media

0%

Formal & Business

0%

Ludwig's WRAP-UP

The term "sham data" is a grammatically sound and frequently employed phrase, particularly within scientific discourse, as Ludwig AI confirms. It denotes data that is not genuine, serving various purposes from experimental controls to identifying fraudulent research. Related phrases like "fake data" or "fraudulent data" can be used, though they carry slightly different connotations. When using the phrase, it's crucial to provide context, clarifying why data is classified as such and avoiding premature assumptions about its nature. The widespread use of "sham data" in academic and scientific publications underscores its importance in maintaining data integrity and transparency. It's worth noting that no contrasting usage data exists in the provided examples.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

fake data

Replaces "sham" with a more common synonym, implying the data is not genuine.

false data

Substitutes "sham" with "false", directly indicating the data is untrue.

fraudulent data

Emphasizes the deceptive nature of the data, suggesting it was intentionally fabricated.

invalid data

Focuses on the data's lack of validity or correctness.

erroneous data

Highlights the presence of errors or inaccuracies in the data.

bogus data

Uses a more informal term to describe data that is not genuine or reliable.

spurious data

Indicates that the data appears to be genuine but is actually false or misleading.

simulated data

Suggests the data was artificially created to resemble real data.

artificial data

Emphasizes the data's non-natural origin, implying it was created or manipulated.

contrived data

Implies the data was deliberately created or manipulated for a specific purpose.

FAQs

How is "sham data" used in scientific research?

"Sham data" is often used as a control in experiments, representing a condition where no real effect is expected. It helps researchers differentiate between genuine results and background noise or experimental artifacts.

What's the difference between "sham data" and "fraudulent data"?

"Sham data" generally refers to data that is not genuine, regardless of intent. "Fraudulent data", on the other hand, implies a deliberate intent to deceive or misrepresent findings.

What are some alternatives to using the term "sham data"?

Depending on the context, you can use alternatives like "fake data", "false data", "invalid data", or "erroneous data". Choose the term that best reflects the specific nature of the data's inaccuracy.

How can I identify "sham data" in a dataset?

Identifying "sham data" often involves comparing it to known benchmarks, looking for inconsistencies or anomalies, and verifying the data's source and collection methods. Statistical analysis can also help detect patterns that suggest the data is not genuine.