Sentence examples for biased conclusion from inspiring English sources

Nonetheless, we have established the increased risk for women compared with men; thus, our study is unlikely to have a biased conclusion.

The resulting file tracks a cohort of individual patients through the system across primary and secondary healthcare organisational boundaries, and avoids the potential biased conclusion through the analysis of different cohorts of patients from separate cross-sectional databases.

Besides, statistical analsys is alway skewed to prove the point of the person makeing the claim rather than finding out the raw data and then coming up with a non biased conclusion.

Obviously, if you consider only one side of that equation or the other as most amateur analysts do you reach a biased conclusion.

An emphasis in the NTFP literature on studying the population dynamics of rare and declining species could lead to the biased conclusion that ecological interactions and ecosystem-level effects resulting from harvest are generally unimportant.

"Obtaining a biased conclusion using the wrong methodology has inflicted irreparable and serious damage to Taobao and Chinese online businesses".. Alibaba said last month that it had removed 90 million listings for potentially counterfeit goods over the first nine months of 2014, and had spent more than $160m on efforts to protect its customers since 2013.

For example, motile mitochondria in the dynamic pause state might be incorrectly identified as stationary, resulting in possibly biased conclusions.

One of the reasons researchers use systematic reviews and meta-analysis is to avoid potentially biased conclusions that can arise from standard narrative reviews (Wilson 2001).

Had we analyzed only one source of information about rejections or relied only on quantitative data, we could have reached incomplete and biased conclusions.

Our observations also suggest that simply computing the average speed or running length, as is the case of many previous studies, may lead to a remarkable loss of algorithm sensitivity and therefore biased conclusions.

However, both of these interpretations are patently incorrect and are couched in misunderstandings and misconceptions (that is, inherently biased conclusions always result from false assumptions and/or incorrect data).