find one qualitative and one quantitative study. Summarize each study using short paragraphs and discuss and evaluate the d

Crop Res. 56 (1 & 2) : 45-49 (2021) Printed in India

Genetic studies for quantitative traits in pigeonpea (Cajanus cajan) M. S. ASWINI, SHOBICA PRIYA RAMASAMY AND A. THANGA HEMAVATHY*

Department of Pulses Tamil Nadu Agricultural University, Coimbatore-641003, Tamil Nadu, India

*(e-mail : [email protected])

(Received : December 15, 2021/Accepted : March 16, 2021)

ABSTRACT

Pigeonpea is one of the most important pulse crops grown in many states of India and plays an important role in sustainable food and nutritional security. Increased pigeonpea production is possible with high yielding varieties suitable for different agro-climatic conditions, that can be achieved by assessing the variability of the existing germplasm. Based on this, a study was carried out during kharif 2018 at Department of Pulses, Tamil Nadu Agricultural University, Coimbatore, India to evaluate the genetic variability, heritability and genetic advance of eleven characters in fifteen genotypes including checks (Gulyal Red, CO 6, CO 8, Maruti, BWR 133, BSMR 846, ICPL 10967, ICPL 11023, IC 525403, IC 525411, IC 525413, IC 525424, IC 525456, IC 525521, IC 525527). Analysis of variance revealed that significant difference among genotypes for all the eleven characters studied the magnitude of PCV and GCV was moderate to high for racemes per plant, cluster per plant, pods per cluster, total number of pods and single plant weight. High heritability was recorded for primary branches, pod length, racemes per plant, cluster per plant, number of pods per plant, 100 seed weight, plant height and single plant weight. High heritability combined with high genetic advance was recorded for number of pods per plant, clusters per plant and plant height indicating that these characters are controlled by additive gene effect and phenotypic selection of these characters would be effective for further breeding purpose.

Key Words : Genetic advance, genetic variability, heritability, pigeonpea, quantitative traits

INTRODUCTION

Pigeonpea (Cajanus cajan L. Mill) is the second important pulse crop after chickpea (Cicer arietinum L.) in India. Pigeonpea is a drought tolerant and hardy crop (Maiti and Singh, 2016). It has a wide range of maturity that helps in its adaption in a wide range of environments and cropping systems (Khadtare et al., 2017; Hari et al., 2018; Kaur and Saini, 2018). This crop occupies an essential place in our daily diet as very high-quality source of protein and it is mainly cultivated for its dry seeds and green pods (Vijayalakshmi et al., 2013). I ndia ran ks fi rs t in pig e o npe a cultivation area (5.58 mha) and production (4.29 mt) in the world (FAOSTAT, 2020). The per capita availability of protein in the country is 28 g/day, while WHO recommendation is 80 g/day, and there exists a most serious problem of the malnutrition among the people who are mostly relying upon vegetarian diet without consuming animal protein (Prasad et

al., 2013; Saxena et al., 2020). In the last five years, productivity of pigeonpea in India has shown an increasing trend (11.42% ) from 693 (2009 – 2013) to 774 kg/ha (2014 – 2018). However, it is lowered by ~10% compared to world productivity (761 kg/ha in 2009 – 2013 and 850 kg/ha in 2014 – 2018) (FAOSTAT, 2020).

The annual demand for pulses is increasing by 3% . Since the demand for pi ge on pe a is e ve r i nc re as in g an d are a available for expansion is limited, research now needs to focus on the genetic enhancement of yield. Considering the importance of pigeonpea in fulfilling nutritional requirements of ever- increasing population of our country with reducing availability of land resources and low productivity level of pigeonpea varieties, there is an urgent need to develop high yielding varieties of pigeonpea adapted to different types of agro-climatic conditions especially the marginal land and stress environments.

Improv e me n t in yi e l d, pri mari ly

DOI : 10.31830/2454-1761.2021.008

depends on the magnitude and the nature of genetic variability present in the population. The knowledge of character associations between yield and its components might provide useful information on nature, extent and direction of selection. The seed yield of pigeonpea is a complex and multiplicative character, which is highly influenced by environmental variations. Information on nature and magnitude of variability present in a population due to genetic and non-genetic cause s is an important pre re quisite for sy ste matic bre e di ng pro gramme . I t is the refore, ne cessary to estimate relative amounts of genetic and non-genetic variability e xhibite d by diffe re nt characte rs using suitable parameters like genetic coefficient of variability (GCV), broad sense heritability estimates (H² (Broad Sense)) and genetic advance (GA) and genetic advance as percent mean (GAM). The information on their genetic variability and heritability are of considerable importance in selection of elite genotype as well as exploitation of heterosis breeding programme (Anuradha and Patro, 2019). Hence, an attempt was made in the present investigation to ascertain the variability, heritability and genetic advance of some quantitative characters in a set of pigeonpea genotypes.

MATERIALS AND METHODS

The present investigation was carried during kharif season of 2018 at the Department of Pulses, Tamil Nadu Agricultural University, Coimbatore, India to assess the variability among 15 genotypes of pigeonpea.

The experimental location was situated at an altitude of 427 m above MSL, latitude of 11o N and a longitude of 77o E. The average annual rainfall received at this location was around 700 mm. Fifteen pigeonpea genotypes we re g ro wn in 2 row s of 4m l e n gth in randomized complete block design (RBD) with three replications. Row to row and plant to plant spacing were maintained at 90 × 30 cm, respectively. Observations were recorded for eleven traits including days to 50% flowering, plant height (cm), number of primary branches per plant, racemes per plant, pod length (cm), pod bearing length (cm), number of clusters per plant, number of pods per cluster, number of pods per plant, 100-seed weight (g) and single

plant yield (g). The data were collected on five randomly selected competitive plants from each replication. The analysis of variance (Table 1) for yield and yield contributing characters was carried out as suggested by Panse and Sukhatme (1985). The coefficient of variation was calculated as per Burton (1952) . Th e g e n otypic and ph e n otypic coefficients of variation were calculated as per the formula suggested by Burton (1952). He ritability in broad sense and ge netic advance were calculated as per Johnson et al. (1955). The genetic advance (GA) as percent of mean was calculated using the following equation :

Genetic advance GA (% ) : ––––––––––––––––––––––– x 100

Grand mean

RESULTS AND DISCUSSION

The acco mplis hme n t of any c rop improvement depends upon the extent of genetic variability in the base population and it is essential to subject for the selection and improve me nt of parti cular trait. In the present study the analysis of variation shown highly significant differe nces among the genotypes for all the characters studied viz., days to 50% flowering, plant height (cm), numbe r of primary branche s per plant, racemes per plant, pod length (cm), pod bearing length (cm), number of clusters per plant, number of pods per cluster, number of pods per plant, 100 seed weight (g) and single plant yield (g), indicating the existence of c o n s ide rabl e ge n e ti c v ari ati o n in th e experimental material. This variability can be utilized effectively to develop high yielding cultivars through hybridization followed by se le ction. Perusal of the components of v ari anc e re v e al e d that th e ph e no ty pi c coefficient of variation (PCV% ) were higher than genotypic coefficient of variation (GCV% ) for all the characters studied indicating the role of environmental variance in the total v ari an c e . Ran ge , me an, co e ffi ci e nts o f v ariatio ns (CV% ) , stan dard e rror (S E) , phenotypic coefficients of variations (PCV% ), genotypic coefficients of variations (GCV% ), heritability, genetic advance (GA) and genetic advan ce as pe rce nt of me an for e le ve n characters presented in Table 2.

46 Aswini, Ramasamy and Hemavathy

T a b le

1 .

A n

a ly

si s

of v

ar ia

n ce

( A

N O

V A

) fo

r yi

el d a

n d y

ie ld

r el

a te

d t

ra it

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p ig

eo n

p ea

c u

lt iv

ar s

P a ra

m et

er s

d .

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ay s

to P

ri m

a ry

R a ce

m es

/ P

o d

P la

n t

C lu

st er

/ P

o d

s /

T ot

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n o.

P od

b ea

ri n

g 1

0 0

-s ee

d S

in g le

5 0

% b ra

n ch

es p

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t le

n g th

h ei

g h

t p

la n

t cl

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er of

p od

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n g th

w ei

g h

t p

la n

t fl

o w

e ri

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(c m

) (c

m )

(c m

) (g

) w

ei g h

t (g

)

G en

o ty

p e

1 4

3 4

4 4

.1 8

9 4

7 .2

8 6

0 9

6 .8

0 5

3 .2

9 1

6 8

7 4

.1 8

8 0

4 3

3 7

.1 4

1 4

.4 0

4 2

1 3

9 1

3 .1

4 1

3 9

1 7

.9 2

2 9

1 .0

1 0

.3 8

E rr

or 5

6 2

5 1

5 .5

4 3

5 6

.0 5

1 6

6 1

.0 6

1 8

.0 2

3 0

4 7

.8 1

2 3

2 5

7 4

.4 5

2 2

.4 0

1 1

6 9

4 8

6 .0

5 1

2 0

6 0

.6 1

3 .9

3 0

.0 1

T h

e va

lu es

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= 0 .0

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0 1 l

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y.

T a b le

2 .

E st

im a ti

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f ge

n et

ic p

ar a m

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s fo

r yi

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on tr

ib u

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p ig

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p ea

c u

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P a ra

m et

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g e

C V

S E

G M

G en

o ty

p ic

P h

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p ic

G C

V P

C V

H er

it a b il

it y

G A

G A

M (%

) va

ri a n

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ri a n

ce (%

) (%

) (%

)

D a ys

t o

5 0 %

f lo

w er

in g

5 5 .6

7 -1

6 4

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5 .6

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1 1

8 .9

4 4

0 .2

1 8

5 .1

3 5

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7 .7

5 4

7 .2

3 8

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7 .5

4 P ri

m a ry

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n ch

es 3

.4 3

-2 7 .6

7 1

1 .8

7 1

.1 2

2 1

.2 4

1 2

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1 8

.. 6

1 1

6 .4

8 2

0 .3

1 6

5 .8

5 5

.8 5

2 7

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R a ce

m es

/ p

la n

t 1

1 .0

-6 5 .6

6 1

8 .9

1 2

.4 3

2 8

.8 0

8 1

.1 6

1 1

0 .8

2 3

1 .2

8 3

6 .5

5 7

3 .2

3 1

5 .8

8 5

5 .1

4 P od

l en

gt h

( cm

) 3

.3 9

-6 .1

1 1

0 .0

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5 .6

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.6 9

1 .0

1 1

4 .7

8 1

7 .8

8 6

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P la

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(c m

) 5

6 .6

3 -2

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1 2

3 0

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4 .6

0 8

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9 .9

8 8

0 .8

7 2

8 .1

0 1

6 .6

2 C

lu st

er /

p la

n t

4 4

.0 -4

6 0 .1

2 3

0 .7

2 2

8 .8

2 2

0 9

.7 7

1 0

6 5

9 .9

0 1

4 8

1 3

.0 2

4 9

.2 1

5 8

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7 1

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1 8

0 .4

2 8

6 .0

0 P

od s/

cl u

st er

1 .0

-4 .1

2 2

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5 0

.2 8

2 .4

0 0

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4 .7

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0 .2

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1 0

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1 4

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T ot

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o. o

f p od

s 6

0 .8

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6 4

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8 .3

0 5

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2 2

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5 .7

0 5

0 .5

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1 7

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1 6

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.8 6

P od

b ea

ri n

g le

n gt

h (

cm )

9 7

.0 -2

0 8 .3

6 1

0 .8

6 6

.5 6

1 3

5 .0

8 1

5 5

.7 5

3 7

1 .1

2 9

.2 3

1 4

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4 1

.9 6

1 6

.6 5

1 2

.3 2

1 0 0 s

ee d w

ei gh

t (g

) 5

.6 3

-1 8 .3

7 2

.6 8

0 .1

1 9

.8 8

4 .1

4 4

.2 1

2 0

.6 0

2 0

.7 7

9 8

.3 3

4 .1

5 4

2 .0

8 S

in gl

e p la

n t

W ei

gh t

(g )

6 0 .0

0 -1

9 4

.9 5

5 .1

6 0

.0 0

6 3

8 .9

9 0

.0 0

5 0

.0 0

5 2

8 .3

8 2

8 .8

5 9

6 .7

9 0

.1 5

5 7

.5 3

Quantitative traits in pigeonpea genotypes 47

Magn itude of PCV and G CV w as moderate to high for racemes per plant (31.2, 36.5), cluster per plant (49.2,58.0), pods per cluster (14.7,30.2), total number of pods (50.5,59.2) an d s ing le pl ant we igh t ( g) (28.3,28.8). These results were in conformity with that of Satish Kumar et al. (2006), Firoz- mahamad et al. (2006) and Badru (2011). Although GCV is indicative of the presence of high degree of genetic variation, the amount of heritable portion can only be determined with the help of heritability estimates and genetic gain. In the present study, heritability in broad sense was high in most of the characters viz., primary branches (65.8% ), pod length (68.4% ), racemes per plant (73.2% ), cluster per plant (71.9% ), number of pods per plant (72.8% ), 100 seed weight (98.3% ), plant height (80.8% ) and single plant weight (96.7% ). The high genetic advance was recorded in characters viz., racemes per plant (15.8% ), plant height (28.1), cluster per plant (180.4), total number of pods per plant (416.1). High heritability combine d with high ge ne tic advance was recorded for number of pods per plant, cluster per plant and plant height.

According to Johnson et al. (1955), heritability estimates along with the genetic gain are usually more useful. High heritability coupled with high genetic advance as percent of mean was more for number of pods per plant, cluster per plant and plant height indicating the role of additive gene in expressing these trai ts , su gg e s ti ng th e be tte r s co pe for improvement of these traits through direct selection. Jagan Mohan Rao and Thirumala Rao (2015) also reported the similar results in pigeonpea.

CONCLUSION

Magnitude of phenotypic coefficients of variation in selected pigeonpea genotypes was higher than genotypic coefficients of variation, indicating that environmental factors are influencing the studied characters. High heritability was recorded for primary branches, pod length, racemes per plant, cluster per plant, number of pods per plant, 100 seed weight and single plant yield. High heritability combined with high genetic advance was recorded for number of pods per plant, cluster per plant and plant height. Hence, selections based on these

traits could improve productivity in pigeonpea directly.

REFERENCES

Anuradha, N. and Patro, T. S. S. K. (2019) Genetic variability, heritability and correlation in advanced red gram genotypes. Int. J. Chem. Stud. 7 : 2964-66.

Badru, D. (2011). Genetic studies in pigeonpea (Cajanus cajan (L.) Mill sp). Electron. J. Plant Breed. 2 : 132-34.

Burton, G. W. (1952). Quantitative inheritance in grasses. Proc. 6th Grassland Congr. 1 : 356- 63.

Firoz-mahamad, Gowda, M. B. and Girish, G., (2006). Genetic variability and association studies in vegetable pigeonpea. J. Envirn. Ecol. 24 : 1124-29.

FAOSTAT (2020). Food and Agriculture Organization of the United Nations Database. Available online: http://faostat.fao.org/database.

Hari, S. N., Sreenivas, G., Jagannadham, J., Amarajyothi, P., Rajasekhar, Y., Swathi, B. (2018). Genetic variability, correlation and path analysis for seed yield and its components in redgram [Cajanus cajan (L.) Millsp.] Bull. Env. Pharmacol. Life Sci. 7 : 53-57.

Jagan Mohan Rao, P. and Thirumala Rao, V. (2015). Ge ne t ic anal ysi s fo r y ie l d and i ts Components in pigeonpea (Cajanus cajan (L.) Millsp). Int. J. Appl. Biol. Pharm. 6 : 189- 90.

Johnson, H. W., Robinson, H. F. and Comstock, R. E. (1955). Estimates of genetic and environmental variability in soyabean. J. Agron. 47 : 314-18.

Kaur, K. and Saini, K. S. (2018). Performance of pigeonpea (Cajanus cajan L.) under different row spacings and genotypes. Crop Res. 53 : 135-37.

Khadtare, S. V., Takate, A. S. and Rajguru, A. B. (2 017) . C omparati ve e val uat ion of pigeonpea (Cajanus cajan L.) under different planting techniques in dryland conditions of Maharashtra. Res. Crops 18 : 35-41.

Maiti, R. K. and Singh, V. P. (2016). Mechanisms of resistance to drought, temperature and salinity in bean crops. Farm. Manage. 1 : 134-61.

Panse , V. G. and Sukhatme , P . V . (1 985 ). Statistical methods for agricultural workers (4th Edn). Indian Council of Agricultural Research, New Delhi.

48 Aswini, Ramasamy and Hemavathy

Prasad, Y., Kumar, K., Mishra, S. B. (2013). Studies on ge ne ti c param e te rs and inte r- re lati ons hip s am ong yie ld and yie ld contributing traits in pigeonpea [Cajanus cajan (L.) Millsp.]. The Bioscan. 8 : 207-11.

Rachit, K. Saxena, Anil Hake, Anupama J. Hingane, Sameer Kumar, C. V., Abhishek Bohra, Muniswamy Sonnappa, Abhishek Rathore, Anil, V. Kumar., Anil Mishra, Tikle. A. N., Chourat Sudhakar, Rajamani, S., Patil, D. K., Singh, I. P., Singh, N. P. and Rajeev, K. Var shne y ( 2020 ). Translational pi ge onpe a ge nomic s c onso rti um f or

accelerating genetic gains in pigeonpea (Cajanus cajan L.). Agron. : doi:10.3390/ agronomy10091289.

Satish Kumar, D., Koteswara Rao, Y., Rama Kumar, P. V. and Srinivasa Rao, V. (2006). Ge ne ti c v ariabili ty and charact e r association in pigeonpea (Cajanus cajan (L.) Mill sp.). J. Andhra Agric. 1 & 2 : 116-18.

Vijayalakshmi, P., Anuradha, Ch., Pavan Kumar, D., Srilakshmi, A. and Anuradha, G. (2013). Path coefficient and correlation response for yield attributes in Pigeonpea (Cajanus cajan L.). Int. J. Scient. Res. Pub. 3 : 109-13.

Quantitative traits in pigeonpea genotypes 49

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RESEARCH ARTICLE

Why we habitually engage in null-hypothesis

significance testing: A qualitative study

Jonah StuntID 1,2*, Leonie van Grootel1,3, Lex BouterID

4,5 , David Trafimow

6 ,

Trynke Hoekstra 1 , Michiel de Boer

1,7

1 Department of Health Sciences, Section of Methodology and Applied Statistics, Vrije Universiteit,

Amsterdam, The Netherlands, 2 Department of Radiation Oncology, Erasmus Medical Center, Rotterdam,

The Netherlands, 3 Rathenau Institute, The Hague, The Netherlands, 4 Department of Philosophy, Vrije

Universiteit, Amsterdam, The Netherlands, 5 Department of Epidemiology and Data Science, Amsterdam

University Medical Centers, Amsterdam, The Netherlands, 6 Psychology Department, New Mexico State

University, Las Cruces, New Mexico, United States of America, 7 Department of General Practice and Elderly

Care, University Medical Center Groningen, Groningen, The Netherlands

* [email protected]

Abstract

Background

Null Hypothesis Significance Testing (NHST) is the most familiar statistical procedure for

making inferences about population effects. Important problems associated with this

method have been addressed and various alternatives that overcome these problems have

been developed. Despite its many well-documented drawbacks, NHST remains the prevail-

ing method for drawing conclusions from data. Reasons for this have been insufficiently

investigated. Therefore, the aim of our study was to explore the perceived barriers and facili-

tators related to the use of NHST and alternative statistical procedures among relevant

stakeholders in the scientific system.

Methods

Individual semi-structured interviews and focus groups were conducted with junior and

senior researchers, lecturers in statistics, editors of scientific journals and program leaders

of funding agencies. During the focus groups, important themes that emerged from the inter-

views were discussed. Data analysis was performed using the constant comparison

method, allowing emerging (sub)themes to be fully explored. A theory substantiating the

prevailing use of NHST was developed based on the main themes and subthemes we

identified.

Results

Twenty-nine interviews and six focus groups were conducted. Several interrelated facilita-

tors and barriers associated with the use of NHST and alternative statistical procedures

were identified. These factors were subsumed under three main themes: the scientific cli-

mate, scientific duty, and reactivity. As a result of the factors, most participants feel depen-

dent in their actions upon others, have become reactive, and await action and initiatives

PLOS ONE

PLOS ONE | https://doi.org/10.1371/journal.pone.0258330 October 15, 2021 1 / 23

a1111111111

a1111111111

a1111111111

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OPEN ACCESS

Citation: Stunt J, van Grootel L, Bouter L,

Trafimow D, Hoekstra T, de Boer M (2021) Why we

habitually engage in null-hypothesis significance

testing: A qualitative study. PLoS ONE 16(10):

e0258330. https://doi.org/10.1371/journal.

pone.0258330

Editor: Hisham Zerriffi, University of British

Columbia, CANADA

Received: November 20, 2020

Accepted: September 24, 2021

Published: October 15, 2021

Copyright: © 2021 Stunt et al. This is an open access article distributed under the terms of the

Creative Commons Attribution License, which

permits unrestricted use, distribution, and

reproduction in any medium, provided the original

author and source are credited.

Data Availability Statement: A full study protocol,

including a detailed data analysis plan, was

preregistered (https://osf.io/4qg38/). At the start of

this study, preregistration forms for qualitative

studies were not developed yet. Therefore,

preregistration for this study is based on an

outdated form. Presently, there is a preregistration

form available for qualitative studies. Information

about data collection, data management, data

sharing and data storage is described in a Data

Management Plan. Sensitive data is stored in

Darkstor, an offline archive for storing sensitive

from others. This may explain why NHST is still the standard and ubiquitously used by

almost everyone involved.

Conclusion

Our findings demonstrate how perceived barriers to shift away from NHST set a high thresh-

old for actual behavioral change and create a circle of interdependency between stakehold-

ers. By taking small steps it should be possible to decrease the scientific community’s

strong dependence on NHST and p-values.

Introduction

Empirical studies often start from the idea that there might be an association between a specific

factor and a certain outcome within a population. This idea is referred to as the alternative

hypothesis (H1). Its complement, the null hypothesis (H0), typically assumes no association or

effect (although it is possible to test other effect sizes than no effect with the null hypothesis).

At the stage of data-analysis, the probability of obtaining the observed, or a more extreme,

association is calculated under the assumption of no effect in the population (H0) and a num-

ber of inferential assumptions [1]. The probability of obtaining the observed, or more extreme,

data is known as ‘the p-value’. The p-value demonstrates the compatibility between the

observed data and the expected data under the null hypothesis, where 0 is complete incompati-

bility and 1 is perfect compatibility [2]. When the p-value is smaller than a prespecified value

(labelled as alpha, usually set at 5% (0.05)), results are generally declared to be statistically sig-

nificant. At this point, researchers commonly reject the null hypothesis and accept the alterna-

tive hypothesis [2]. Assessing statistical significance by means of contrasting the data with the

null hypothesis is called Null Hypothesis Significance Testing (NHST). NHST is the best

known and most widely used statistical procedure for making inferences about population

effects. The procedure has become the prevailing paradigm in empirical science [3], and reach-

ing and being able to report statistically significant results has become the ultimate goal for

many researchers.

Despite its widespread use, NHST and the p-value have been criticized since its inception.

Numerous publications have addressed problems associated with NHST and p-values. Argu-

ably the most important drawback is the fact that NHST is a form of indirect or inverse infer-

ence: researchers usually want to know if the null or alternative hypothesis can be accepted

and use NHST to conclude either way. But with NHST, the probability of a finding, or more

extreme findings, given the null hypothesis is calculated [4]. Ergo, NHST doesn’t tell us what we want to know. In fact, p-values were never meant to serve as a basis to draw conclusions,

but as a continuous measure of incompatibility between empirical findings and a statistical

model [2]. Moreover, the procedure promotes a dichot

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